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Search results “Privacy preserving data mining models and algorithms”
Privacy Preserving DataMining
 
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Google TechTalks July 28, 2006 Matthew Roughan joined the School of Applied Mathematics at the University of Adelaide in February 2004, where he is interested in the area of design, and installation of Internet measurement equipment, and the analysis and modeling of Internet measurement data. ABSTRACT The rapid growth of the Internet over the last decade has been startling. However, efforts to track its growth have often fallen afoul of bad data --- for instance, how much traffic does the Internet now carry? The problem is not that the data is technically hard to obtain, or that it does not exist, but rather that the data is not shared. Obtaining an overall picture requires data from multiple sources, few of whom are open to sharing such data, either because it violates privacy legislation, or exposes business secrets. The approaches used so far in the Internet, e.g., trusted third parties, or data anonymization, have been only partially successful, and are not widely adopted. The paper presents a method for performing computations on shared data without any participants revealing their secret data. For example, one can compute the sum of traffic over a set of service providers without any service provider learning the traffic of another. The method is simple, scalable, and flexible enough to perform a wide range of valuable operations on Internet data. Google engEDU
Views: 3660 GoogleTalksArchive
Introduction to data mining and architecture  in hindi
 
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#datamining #datawarehouse #lastmomenttuitions Take the Full Course of Datawarehouse What we Provide 1)22 Videos (Index is given down) + Update will be Coming Before final exams 2)Hand made Notes with problems for your to practice 3)Strategy to Score Good Marks in DWM To buy the course click here: https://lastmomenttuitions.com/course/data-warehouse/ Buy the Notes https://lastmomenttuitions.com/course/data-warehouse-and-data-mining-notes/ if you have any query email us at [email protected] Index Introduction to Datawarehouse Meta data in 5 mins Datamart in datawarehouse Architecture of datawarehouse how to draw star schema slowflake schema and fact constelation what is Olap operation OLAP vs OLTP decision tree with solved example K mean clustering algorithm Introduction to data mining and architecture Naive bayes classifier Apriori Algorithm Agglomerative clustering algorithmn KDD in data mining ETL process FP TREE Algorithm Decision tree
Views: 225551 Last moment tuitions
SecureML: A System for Scalable Privacy-Preserving Machine Learning
 
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SecureML: A System for Scalable Privacy-Preserving Machine Learning Yupeng Zhang (University of Maryland) Presented at the 2017 IEEE Symposium on Security & Privacy May 22–24, 2017 San Jose, CA http://www.ieee-security.org/TC/SP2017/ ABSTRACT Machine learning is widely used in practice to produce predictive models for applications such as image processing, speech and text recognition. These models are more accurate when trained on large amount of data collected from different sources. However, the massive data collection raises privacy concerns. In this paper, we present new and efficient protocols for privacy preserving machine learning for linear regression, logistic regression and neural network training using the stochastic gradient descent method. Our protocols fall in the two-server model where data owners distribute their private data among two non-colluding servers who train various models on the joint data using secure two-party computation (2PC). We develop new techniques to support secure arithmetic operations on shared decimal numbers, and propose MPC-friendly alternatives to nonlinear functions such as sigmoid and softmax that are superior to prior work. We implement our system in C++. Our experiments validate that our protocols are several orders of magnitude faster than the state of the art implementations for privacy preserving linear and logistic regressions, and scale to millions of data samples with thousands of features. We also implement the first privacy preserving system for training neural networks.
Final Year Projects | Comparison and evaluation of data mining techniques with algorithmic models
 
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Views: 313 Clickmyproject
Privacy Preserving Data Encryption Strategy for Big Data in Mobile Cloud Computing
 
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Privacy Preserving Data Encryption Strategy for Big Data in Mobile Cloud Computing To get this project in ONLINE or through TRAINING Sessions, Contact: JP INFOTECH, Old No.31, New No.86, 1st Floor, 1st Avenue, Ashok Pillar, Chennai -83.Landmark: Next to Kotak Mahendra Bank. Pondicherry Office: JP INFOTECH, #37, Kamaraj Salai,Thattanchavady, Puducherry -9.Landmark: Next to VVP Nagar Arch. Mobile: (0) 9952649690, Email: [email protected], web: http://www.jpinfotech.org Privacy has become a considerable issue when the applications of big data are dramatically growing in cloud computing. The benefits of the implementation for these emerging technologies have improved or changed service models and improve application performances in various perspectives. However, the remarkably growing volume of data sizes has also resulted in many challenges in practice. The execution time of the data encryption is one of the serious issues during the data processing and transmissions. Many current applications abandon data encryptions in order to reach an adoptive performance level companioning with privacy concerns. In this paper, we concentrate on privacy and propose a novel data encryption approach, which is called Dynamic Data Encryption Strategy (D2ES). Our proposed approach aims to selectively encrypt data and use privacy classification methods under timing constraints. This approach is designed to maximize the privacy protection scope by using a selective encryption strategy within the required execution time requirements. The performance of D2ES has been evaluated in our experiments, which provides the proof of the privacy enhancement.
Views: 306 jpinfotechprojects
Data Mining and Privacy
 
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Made with http://biteable.com
Views: 156 Jason Alaee
Privacy-Preserving Selective Aggregation of Online User Behavior Data
 
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Privacy-Preserving Selective Aggregation of Online User Behavior Data in Dot Net To get this project in ONLINE or through TRAINING Sessions, Contact: JP INFOTECH, Old No.31, New No.86, 1st Floor, 1st Avenue, Ashok Pillar, Chennai -83.Landmark: Next to Kotak Mahendra Bank. Pondicherry Office: JP INFOTECH, #37, Kamaraj Salai,Thattanchavady, Puducherry -9.Landmark: Next to VVP Nagar Arch. Mobile: (0) 9952649690, Email: [email protected], web: http://www.jpinfotech.org Tons of online user behavior data are being generated every day on the booming and ubiquitous Internet. Growing efforts have been devoted to mining the abundant behavior data to extract valuable information for research purposes or business interests. However, online users’ privacy is thus under the risk of being exposed to third-parties. The last decade has witnessed a body of research works trying to perform data aggregation in a privacy-preserving way. Most of existing methods guarantee strong privacy protection yet at the cost of very limited aggregation operations, such as allowing only summation, which hardly satisfies the need of behavior analysis. In this paper, we propose a scheme PPSA, which encrypts users’ sensitive data to prevent privacy disclosure from both outside analysts and the aggregation service provider, and fully supports selective aggregate functions for online user behavior analysis while guaranteeing differential privacy. We have implemented our method and evaluated its performance using a trace-driven evaluation based on a real online behavior dataset. Experiment results show that our scheme effectively supports both overall aggregate queries and various selective aggregate queries with acceptable computation and communication overheads.
Views: 141 jpinfotechprojects
Privacy-preserving record linkage in healthcare
 
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Elizabeth Ashley Durham discusses the steps required to perform privacy-preserving record linkage and discuss open research challenges. Record linkage is the task of identifying records from disparate data sources that refer to the same individual. Specific applications in health care are sharing patient data for research and aggregating patient data from multiple providers to improve patient care. A variant of record linkage, known as privacy-preserving record linkage, is required such that records referring to the same individual are identified without ever revealing the content of the records. Ms. Durham is a Ph.D. student in Biomedical Informatics at Vanderbilt University.
Views: 1581 eHealthInfoLab
SD IEEE Dotnet 11 A Random Decision Tree Framework for Privacy-Preserving Data Mining
 
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Distributed Data Mining with Differential Privacy
 
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Distributed Data Mining with Differential Privacy To get this project in ONLINE or through TRAINING Sessions, Contact: JP INFOTECH, Old No.31, New No.86, 1st Floor, 1st Avenue, Ashok Pillar, Chennai -83.Landmark: Next to Kotak Mahendra Bank. Pondicherry Office: JP INFOTECH, #37, Kamaraj Salai,Thattanchavady, Puducherry -9.Landmark: Next to VVP Nagar Arch. Mobile: (0) 9952649690, Email: [email protected], web: http://www.jpinfotech.org With recent advances in communication and data storage technology, an explosive amount of information is being collected and stored in the Internet. Even though such vast amount of information presents great opportunities for knowledge discovery, organizations might not want to share their data due to legal or competitive reasons. This posts the challenge of mining knowledge while preserving privacy. Current efficient privacy preserving data mining algorithms are based on an assumption that it is acceptable to release all the intermediate results during the data mining operations. However, it has been shown that such intermediate results can still leak private information. In this work, we use differential privacy to quantitatively limit such information leak. Differential privacy is a newly emerged privacy definition that is capable of providing strong measurable privacy guarantees. We propose Secure group Differential private Query(SDQ), a new algorithm that combines techniques from differential privacy and secure multiparty computation. Using decision tree induction as a case study, we show that SDQ can achieve stronger privacy than current efficient secure multiparty computation approaches, and better accuracy than current differential privacy approaches while maintaining efficiency.
Views: 96 jpinfotechprojects
Using Data Mining to Predict Hospital Admissions From the Emergency Department
 
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Using Data Mining to Predict Hospital Admissions From the Emergency Department -- The World Health Organization estimates that by 2030 there will be approximately 350 million young people (below 30 to 40 years) with various diseases associated with renal complications, stroke and peripheral vascular disease. Our aim is to analyze the risk factors and system conditions to detect disease early with prediction strategies. By using the effective methods to identify and extract key information that describes aspects of developing a prediction model, sample size and number of events, risk predictor selection. Crowding within emergency departments (EDs) can have significant negative consequences for patients. EDs therefore need to explore the use of innovative methods to improve patient flow and prevent overcrowding. One potential method is the use of data mining using machine learning techniques to predict ED admissions. This system highlights the potential utility of three common machine learning algorithms in predicting patient admissions. In this proposed approach, we considered a heart disease as a main concern and we start prediction over that disease. Because in India a strategic survey on 2015-6016 resulting that every year half-a million of people suffer from various heart diseases. Practical implementation of the models developed in this paper in decision support tools would provide a snapshot of predicted admissions from the ED at a given time, allowing for advance resource planning and the avoidance bottlenecks in patient flow, as well as comparison of predicted and actual admission rates. When interpretability is a key consideration, EDs should consider adopting logistic regression models, although GBM's will be useful where accuracy is paramount. Using the strategic algorithm such as Logistic Regression, Decision Trees and Gradient Boosted Machine, we can easily identify the disease with various attributes and risk factor specifications. Based on these parameters, the analysis of high risk factors of developing disease is identified using mining principles. Use of data mining algorithms will result in quick prediction of disease with high accuracy. Data mining, emergency department, hospitals, machine learning, predictive models -- For More Details, Contact Us -- Arihant Techno Solutions www.arihants.com E-Mail-ID: [email protected] Mobile: +91-75984 92789
Privacy-Preserving Data Compression
 
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Privacy-Preserving Data Compression Lijuan Cui, MS
Views: 101 Calit2ube
Modelling the network behaviour of malware to block malicious patterns
 
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This presentation by Sebastian Garcia (CTU University, Prague) was delivered at VB2015 in Prague, Czech Republic. Current malware traffic detection solutions work mostly by using static fingerprints, whitelists and blacklists, and crowd-sourced threat intelligence analytics. These methods are useful for detecting known malware in real time, but are insufficient to detect unknown malicious trends and attacks. Our proposed complementary solution is to analyse the inherent patterns of malware actions in the network by means of machine learning algorithms. In particular, we use Markov Chains-based algorithms to find network patterns that are independent of static features, such as IP addresses or payloads. These patterns are used to build behavioural models of malware actions that are later used to detect similar traffic in the network. All these models and detection algorithms were used to create a free software intrusion prevention system, called Stratosphere IPS, which is thoroughly tested with normal and malicious traffic. The IPS is able to detect new network patterns that are similar to the known malicious behaviours. The Stratosphere IPS tool will be used to show how behavioural models can detect real malware traffic.
Views: 247 Virus Bulletin
An Efficient Privacy-Preserving Ranked Keyword Search Method
 
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An Efficient Privacy-Preserving Ranked Keyword Search Method To get this project in ONLINE or through TRAINING Sessions, Contact: JP INFOTECH, Old No.31, New No.86, 1st Floor, 1st Avenue, Ashok Pillar, Chennai -83.Landmark: Next to Kotak Mahendra Bank. Pondicherry Office: JP INFOTECH, #45, Kamaraj Salai,Thattanchavady, Puducherry -9.Landmark: Next to VVP Nagar Arch. Mobile: (0) 9952649690, Email: [email protected], web: http://www.jpinfotech.org, Blog: http://www.jpinfotech.blogspot.com Cloud data owners prefer to outsource documents in an encrypted form for the purpose of privacy preserving. Therefore it is essential to develop efficient and reliable ciphertext search techniques. One challenge is that the relationship between documents will be normally concealed in the process of encryption, which will lead to significant search accuracy performance degradation. Also the volume of data in data centers has experienced a dramatic growth. This will make it even more challenging to design ciphertext search schemes that can provide efficient and reliable online information retrieval on large volume of encrypted data. In this paper, a hierarchical clustering method is proposed to support more search semantics and also to meet the demand for fast ciphertext search within a big data environment. The proposed hierarchical approach clusters the documents based on the minimum relevance threshold, and then partitions the resulting clusters into sub-clusters until the constraint on the maximum size of cluster is reached. In the search phase, this approach can reach a linear computational complexity against an exponential size increase of document collection. In order to verify the authenticity of search results, a structure called minimum hash sub-tree is designed in this paper. Experiments have been conducted using the collection set built from the IEEE Xplore. The results show that with a sharp increase of documents in the dataset the search time of the proposed method increases linearly whereas the search time of the traditional method increases exponentially. Furthermore, the proposed method has an advantage over the traditional method in the rank privacy and relevance of retrieved documents.
Views: 1113 JPINFOTECH PROJECTS
A Supermodularity-Based Differential Privacy Preserving Algorithm for Data Anonymization
 
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To get this project in ONLINE or through TRAINING Sessions, Contact:JP INFOTECH, Old No.31, New No.86, 1st Floor, 1st Avenue, Ashok Pillar, Chennai -83. Landmark: Next to Kotak Mahendra Bank. Pondicherry Office: JP INFOTECH, #45, Kamaraj Salai, Thattanchavady, Puducherry -9. Landmark: Next to VVP Nagar Arch. Mobile: (0) 9952649690 , Email: [email protected], web: www.jpinfotech.org Blog: www.jpinfotech.blogspot.com A Supermodularity-Based Differential Privacy Preserving Algorithm for Data Anonymization Maximizing data usage and minimizing privacy risk are two conflicting goals. Organizations always apply a set of transformations on their data before releasing it. While determining the best set of transformations has been the focus of extensive work in the database community, most of this work suffered from one or both of the following major problems: scalability and privacy guarantee. Differential Privacy provides a theoretical formulation for privacy that ensures that the system essentially behaves the same way regardless of whether any individual is included in the database. In this paper, we address both scalability and privacy risk of data anonymization. We propose a scalable algorithm that meets differential privacy when applying a specific random sampling. The contribution of the paper is two-fold: 1) we propose a personalized anonymization technique based on an aggregate formulation and prove that it can be implemented in polynomial time; and 2) we show that combining the proposed aggregate formulation with specific sampling gives an anonymization algorithm that satisfies differential privacy. Our results rely heavily on exploring the supermodularity properties of the risk function, which allow us to employ techniques from convex optimization. Through experimental studies we compare our proposed algorithm with other anonymization schemes in terms of both time and privacy risk.
Views: 75 jpinfotechprojects
Privacy-Preserving Distributed Multi-Task Learning with Asynchronous Updates
 
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Liyang Xie (Michigan State University) Inci Baytas (Michigan State University) Kaixiang Lin (Michigan State University) Jiayu Zhou (Michigan State University) Many data mining applications involve a set of related learning tasks. Multi-task learning (MTL) is a learning paradigm that improves generalization performance by transferring knowledge among those tasks. MTL has attracted so much attention in the community, and various algorithms have been successfully developed. Recently, distributed MTL has also been studied for related tasks whose data is distributed across different geographical regions. One prominent challenge of the distributed MTL frameworks is to maintain the privacy of the data. The distributed data may contain sensitive and private information such as patient records and registers of a company. In such cases, distributed MTL frameworks are required to preserve the privacy of the data. In this paper, we propose a novel privacy-preserving distributed MTL framework to address this challenge. A privacy-preserving proximal gradient algorithm, which asynchronously updates models of the learning tasks, is introduced to solve a general class of MTL formulations. The proposed asynchronous approach is robust against network delays and provides a guaranteed differential privacy through carefully designed perturbation. Theoretical guarantees of the proposed algorithm are derived and supported by the extensive experimental results. More on http://www.kdd.org/kdd2017/.
Views: 745 KDD2017 video
Data Mining For Risk-Adjusting Healthcare Cost Predictions Part 4
 
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Salford Systems' 2009 Data Mining Conference. John Robinson is presenting in San Diego,CA.
Views: 76 Salford Systems
Privacy Preserving Outsourced Association Rule Mining on Vertically Partitioned Databases
 
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2016 IEEE Transaction on Information Forensics and Security For More Details::Contact::K.Manjunath - 09535866270 http://www.tmksinfotech.com and http://www.bemtechprojects.com 2016 and 2017 IEEE @ TMKS Infotech
Views: 562 manju nath
Enabling Multilevel Trust in Privacy Preserving Data Mining
 
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Enabling Multilevel Trust in Privacy Preserving Data Mining To get this project in ONLINE or through TRAINING Sessions, Contact: JP INFOTECH, Old No.31, New No.86, 1st Floor, 1st Avenue, Ashok Pillar, Chennai -83.Landmark: Next to Kotak Mahendra Bank. Pondicherry Office: JP INFOTECH, #37, Kamaraj Salai,Thattanchavady, Puducherry -9.Landmark: Next to VVP Nagar Arch. Mobile: (0) 9952649690, Email: [email protected], web: http://www.jpinfotech.org Privacy Preserving Data Mining (PPDM) addresses the problem of developing accurate models about aggregated data without access to precise information in individual data record. A widely studied perturbation-based PPDM approach introduces random perturbation to individual values to preserve privacy before data are published. Previous solutions of this approach are limited in their tacit assumption of single-level trust on data miners. In this work, we relax this assumption and expand the scope of perturbation-based PPDM to Multilevel Trust (MLT-PPDM). In our setting, the more trusted a data miner is, the less perturbed copy of the data it can access. Under this setting, a malicious data miner may have access to differently perturbed copies of the same data through various means, and may combine these diverse copies to jointly infer additional information about the original data that the data owner does not intend to release. Preventing such diversity attacks is the key challenge of providing MLT-PPDM services. We address this challenge by properly correlating perturbation across copies at different trust levels. We prove that our solution is robust against diversity attacks with respect to our privacy goal. That is, for data miners who have access to an arbitrary collection of the perturbed copies, our solution prevent them from jointly reconstructing the original data more accurately than the best effort using any individual copy in the collection. Our solution allows a data owner to generate perturbed copies of its data for arbitrary trust levels on demand. This feature offers data owners maximum flexibility.
Talk 1: Privacy-preserving Prediction, Talk 2: Calibrating noise ...
 
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Talk 1: Cynthia Dwork and Vitaly Feldman Privacy-preserving Prediction ABSTRACT. Ensuring differential privacy of models learned from sensitive user data is an important goal that has been studied extensively in recent years. It is now known that for some basic learning problems, especially those involving high-dimensional data, producing an accurate private model requires much more data than learning without privacy. At the same time, in many applications it is not necessary to expose the model itself. Instead users may be allowed to query the prediction model on their inputs only through an appropriate interface. Here we formulate the problem of ensuring privacy of individual predictions and investigate the overheads required to achieve it in several standard models of classification and regression. We first describe a simple baseline approach based on training several models on disjoint subsets of data and using standard private aggregation techniques to predict. We show that this approach has nearly optimal sample complexity for (realizable) PAC learning of any class of Boolean functions. At the same time, without strong assumptions on the data distribution, the aggregation step introduces a substantial overhead. We demonstrate that this overhead can be avoided for the well-studied class of thresholds on a line and for a number of standard settings of convex regression. The analysis of our algorithm for learning thresholds relies crucially on strong generalization guarantees that we establish for all prediction private algorithms. Talk 2: Vitaly Feldman and Thomas Steinke Calibrating Noise to Variance in Adaptive Data Analysis ABSTRACT. Datasets are often used multiple times and each successive analysis may depend on the outcome of previous analyses. Standard techniques for ensuring generalization and statistical validity do not account for this adaptive dependence. A recent line of work studies the challenges that arise from such adaptive data reuse by considering the problem of answering a sequence of ``queries'' about the data distribution where each query may depend arbitrarily on answers to previous queries. The strongest results obtained for this problem rely on differential privacy -- a strong notion of algorithmic stability with the important property that it ``composes'' well when data is reused. However the notion is rather strict, as it requires stability under replacement of an arbitrary data element. The simplest algorithm is to add Gaussian (or Laplace) noise to distort the empirical answers. However, analysing this technique using differential privacy yields suboptimal accuracy guarantees when the queries have low variance. Here we propose a relaxed notion of stability that also composes adaptively. We demonstrate that a simple and natural algorithm based on adding noise scaled to the standard deviation of the query provides our notion of stability. This implies an algorithm that can answer statistical queries about the dataset with substantially improved accuracy guarantees for low-variance queries. The only previous approach that provides such accuracy guarantees is based on a more involved differentially private median-of-means algorithm and its analysis exploits stronger ``group'' stability of the algorithm.
Views: 75 COLT
Privacy-preserving Machine Learning
 
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Prof. Antti Honkela (University of Helsinki), responsible coordinator in FCAI's research program Privacy-preserving and Secure AI, explains some basics of privacy-preserving machine learning methods. Finnish Center for Artificial Intelligence: https://fcai.fi FCAI research programs: https://fcai.fi/research/
Predicting Instructor Performance Using Data Mining Techniques in Higher Education
 
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S3 technologies, 43, North Masi street, Phone: 0452-4373398,9789339435,9500580005 Simmakkal, Madurai Visit: www.s3techindia.com visit:ieeeprojectsmadurai.com Mail: [email protected]
Views: 174 S3 TECHNOLOGIES
Target-Based, Privacy Preserving, and Incremental Association Rule Mining
 
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Greetings from ChennaiSunday Systems Pvt Ltd www.chennaisunday.com Our motto is to bridge the knowledge gap between the academics and the industry.We provide project support for all courses include Ph.D,M.Phil, M.E/M.Tech, B.E/B.Tech, MCA/BCA, MBA/BBA, M.SC/B.Sc and etc.We undertake project works of all major universities 1. BIG DATA – MONGODB WITH NOSQL, JAVA WITH ANGULARJS, NODEJS 2. ANDROID , ANDROID WITH JSON AND PHP , CLOUD IMPLEMENTATION 3. DOT NET MVC FOR RAZOR FRAMEWORK
Views: 51 Siva Kumar
Internet of Things: k-anonymity
 
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Through the internet of things, a lot of data is collected. How can we safely share data with others, without compromising privacy?
Views: 1498 Internet Things
Casey Greene: "Deep learning: privacy preserving data sharing along with some hints and tips"
 
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Computational Genomics Winter Institute 2018 "Deep learning: privacy preserving data sharing along with some hints and tips" Casey Greene, University of Pennsylvania Perelman School of Medicine Institute for Pure and Applied Mathematics, UCLA March 2, 2018 For more information: http://computationalgenomics.bioinformatics.ucla.edu/programs/2018-cgwi/
2011-08-31 CERIAS - Non-homogeneous anonymizations
 
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Recorded: 08/31/2011 CERIAS Security Seminar at Purdue University Non-homogeneous anonymizations Tamir Tassa, The Open University, Israel Privacy Preserving Data Publishing (PPDP) is an evolving research field that is targeted at developing anonymization techniques to enable publishing data so that privacy is preserved while data distortion is minimized. Up until recently most of the research on PPDP considered partition-based anonymization models. The approach in such models is to partition the database records into groups and then homogeneously generalize the quasi-identifiers in all records within a group, as a countermeasure against linking attacks. We describe in this talk alternative anonymization models which are not based on partitioning and homogeneous generalization. Such models extend the set of acceptable anonymizations of a given table, whence they allow achieving similar privacy goals with much less information loss. We shall briefly review the basic models of homogeneous anonymization (e.g. k-anonymity and l-diversity) and then define non-homogeneous anonymization, discuss its privacy, describe algorithms and demonstrate the advantage of such anonymizations in reducing the information loss. We shall then discuss the usefulness of those models for data mining purposes. In particular, we will show that the reduced information loss that characterizes such anonymizations translates also to enhanced accuracy when using the anonymized tables to learn classification models. Based on joint works with Aris Gionis, Arnon Mazza, Mark Last and Sasha Zhmudyak Tamir Tassa is a member of the Department of Mathematics and Computer Science at The Open University of Israel. Previously, he served as a lecturer and researcher in the School of Mathematical Sciences at Tel Aviv University, and in the Department of Computer Science at Ben Gurion University. During the years 1993-1996 he served as an assistant professor of Computational and Applied Mathematics at University of California, Los Angeles. He earned his Ph.D. in applied mathematics from the Tel Aviv University in 1993. His current research interests include cryptography, privacy preserving data publishing and data mining. (Visit: www.cerias.purude.edu)
Views: 194 ceriaspurdue
Incentive Compatible Privacy-Preserving Data Analysis 2013-2014 IEEE
 
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To get this project in ONLINE or through TRAINING Sessions, Contact: JP INFOTECH, 45, KAMARAJ SALAI, THATTANCHAVADY, PUDUCHERRY-9 Landmark: Opposite to Thattanchavady Industrial Estate, Next to VVP Nagar Arch. Mobile: (0) 9952649690 , Email: [email protected], web: www.jpinfotech.org Blog: www.jpinfotech.blogspot.com Incentive Compatible Privacy-Preserving Data Analysis 2013-2014 IEEE In many cases, competing parties who have private data may collaboratively conduct privacy-preserving distributed data analysis (PPDA) tasks to learn beneficial data models or analysis results. Most often, the competing parties have different incentives. Although certain PPDA techniques guarantee that nothing other than the final analysis result is revealed, it is impossible to verify whether participating parties are truthful about their private input data. Unless proper incentives are set, current PPDA techniques cannot prevent participating parties from modifying their private inputs. This raises the question of how to design incentive compatible privacy-preserving data analysis techniques that motivate participating parties to provide truthful inputs. In this paper, we first develop key theorems, then base on these theorems, we analyze certain important privacy-preserving data analysis tasks that could be conducted in a way that telling the truth is the best choice for any participating party.
Views: 600 jpinfotechprojects
CERIAS - 2015-10-21 - Anonymized Data
 
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Anonymized Data Koray Mancuhan - Purdue University Oct 21, 2015 Abstract Privacy has been a hot issue since early 2000s, in particular with the rise of social network and data outsourcing. Data privacy is a big concern in data outsourcing because it involves sharing personal data with third parties. In this talk, I will give an introduction to data privacy on topics such as privacy standards, data anonymization techniques, and data anonymization usage in data outsourcing and data mining. Then, I will present our work in data mining using anonymized data. We propose a data publisher-third party decision tree learning method for outsourced private data. The privacy model is anatomization/fragmentation: the third party sees data values, but the link between sensitive and identifying information is encrypted with a key known only to data publisher. Data publishers have limited processing and storage capability. Both sensitive and identifying information thus are stored on the third parties. The approach presented also retains most processing at the third parties, and data publisher-side processing is amortized over predictions made by the data publishers. Experiments on various datasets show that the method produces decision trees approaching the accuracy of a non-private decision tree, while substantially reducing the data publisher's computing resource requirements. About the Speaker Koray is a PhD student in the Department of Computer Science at Purdue University. He is currently a member of the privacy preserving data mining lab under the supervision of Chris Clifton. His research elaborates the data mining models from the anonymized data. The challenge in his research is the injected uncertainty into data because of anonymization methods. In most cases, uncertainty slows down the data mining models and require special mechanisms to exploit noisy data. His work includes learning algorithms such as k-NN classification, SVM classification, decision tree classification and frequent itemset mining. Koray received his masters degree in Computer Science from Purdue University and his undergraduate degree in Computer Engineering from Galatasaray University. Throughout his masters degree, he studied on data mining and social fairness, and authored papers in this topic. Before joining to Purdue CS, he did his research in semantic web area. He was a former member of Complex Networks lab in Galatasaray University where he worked in developing a new automatic web service annotation tool. http://www.cerias.purdue.edu
Views: 448 ceriaspurdue
evaluation of predictive data mining algorithms in soil data classification for optimized crop recom
 
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evaluation of predictive data mining algorithms in soil data classification for optimized crop recom- IEEE PROJECTS 2018 Download projects @ www.micansinfotech.com WWW.SOFTWAREPROJECTSCODE.COM https://www.facebook.com/MICANSPROJECTS Call: +91 90036 28940 ; +91 94435 11725 IEEE PROJECTS, IEEE PROJECTS IN CHENNAI,IEEE PROJECTS IN PONDICHERRY.IEEE PROJECTS 2018,IEEE PAPERS,IEEE PROJECT CODE,FINAL YEAR PROJECTS,ENGINEERING PROJECTS,PHP PROJECTS,PYTHON PROJECTS,NS2 PROJECTS,JAVA PROJECTS,DOT NET PROJECTS,IEEE PROJECTS TAMBARAM,HADOOP PROJECTS,BIG DATA PROJECTS,Signal processing,circuits system for video technology,cybernetics system,information forensic and security,remote sensing,fuzzy and intelligent system,parallel and distributed system,biomedical and health informatics,medical image processing,CLOUD COMPUTING, NETWORK AND SERVICE MANAGEMENT,SOFTWARE ENGINEERING,DATA MINING,NETWORKING ,SECURE COMPUTING,CYBERSECURITY,MOBILE COMPUTING, NETWORK SECURITY,INTELLIGENT TRANSPORTATION SYSTEMS,NEURAL NETWORK,INFORMATION AND SECURITY SYSTEM,INFORMATION FORENSICS AND SECURITY,NETWORK,SOCIAL NETWORK,BIG DATA,CONSUMER ELECTRONICS,INDUSTRIAL ELECTRONICS,PARALLEL AND DISTRIBUTED SYSTEMS,COMPUTER-BASED MEDICAL SYSTEMS (CBMS),PATTERN ANALYSIS AND MACHINE INTELLIGENCE,SOFTWARE ENGINEERING,COMPUTER GRAPHICS, INFORMATION AND COMMUNICATION SYSTEM,SERVICES COMPUTING,INTERNET OF THINGS JOURNAL,MULTIMEDIA,WIRELESS COMMUNICATIONS,IMAGE PROCESSING,IEEE SYSTEMS JOURNAL,CYBER-PHYSICAL-SOCIAL COMPUTING AND NETWORKING,DIGITAL FORENSIC,DEPENDABLE AND SECURE COMPUTING,AI - MACHINE LEARNING (ML),AI - DEEP LEARNING ,AI - NATURAL LANGUAGE PROCESSING ( NLP ),AI - VISION (IMAGE PROCESSING),mca project NETWORK SECURITY 1. Design, Analysis, and Implementation of ARPKI: An Attack-Resilient Public-Key Infrastructure 2. Efficient and Expressive Keyword Search Over Encrypted Data in Cloud 3. D2 FL: Design and Implementation of Distributed Dynamic Fault Localization 4. On the Interplay Between Cyber and Physical Spaces for Adaptive Security 5. Locating Faults in MANET-Hosted Software Systems 6. Symbolic Synthesis of Timed Models with Strict 2-Phase Fault Recovery 7. Flexible Hardware-Managed Isolated Execution: Architecture, Software Support and Applications 8. SieveQ: A Layered BFT Protection System for Critical Services 9. Searchable Encryption over Feature-Rich Data 10. The g -Good-Neighbor Conditional Diagnosability of Arrangement Graphs 11. CSC-Detector: A System to Infer Large-Scale Probing Campaigns 12. Rmind: A Tool for Cryptographically Secure Statistical Analysis 13. Conditional (t,k) -Diagnosis in Regular and Irregular Graphs Under the Comparison Diagnosis Model 14. End-to-End Detection of Caller ID Spoofing Attacks 15. PDA: Semantically Secure Time-Series Data Analytics with Dynamic User Groups 16. Physical Attestation in the Smart Grid for Distributed State Verification 17. On the Efficiency of FHE-Based Private Queries 18. Shadow Attacks Based on Password Reuses: A Quantitative Empirical Analysis 19. onditional Diagnosability of (n,k) -Star Graphs Under the PMC Model 20. Efficient Anonymous Message Submission 21. Empirical Study of Face Authentication Systems Under OSNFD Attacks 22. Attribute-based Access Control for ICN Naming Scheme 23. Knowledge Connectivity Requirements for Solving Byzantine Consensus with Unknown Participants 24. Understanding Practical Tradeoffs in HPC Checkpoint-Scheduling Policies 25. Risk Assessment in Social Networks Based on User Anomalous Behaviors 26. A Shoulder Surfing Resistant Graphical Authentication System 27. Randomness Tests in Hostile Environments 28. Efficient and Privacy-Preserving Outsourced Calculation of Rational Numbers 29. MADAM: Effective and Efficient Behavior-based Android Malware Detection and Prevention 30. GeTrust: A Guarantee-Based Trust Model in Chord-Based P2P Networks 31. PROVEST: Provenance-Based Trust Model for Delay Tolerant Networks 32. Immutable Authentication and Integrity Schemes for Outsourced Databases 33. Faultprog: Testing the Accuracy of Binary-Level Software Fault Injection 34. Rumor Source Identification in Social Networks with Time-Varying Topology 35. A Systems Theoretic Approach to the Security Threats in Cyber Physical Systems Applied to Stuxnet 36. Magic Train: Design of Measurement Methods against Bandwidth Inflation Attacks 37. Performability Modeling for RAID Storage Systems by Markov Regenerative Process 38. Performability Analysis of k-to-l-Out-of-n Computing Systems Using Binary Decision Diagrams 39. Negative Iris Recognition 40. Randomness Tests in Hostile Environments INTELLIGENT TRANSPORTATION SYSTEMS 1. A new framework of vehicle collision prediction by combining svm and hmm (March 2018 ) 2. Study on Load Balancing of Intermittent Energy Big Data Cloud Platform(09 April 2018) NEURAL NETWORK 1. Efficient kNN Classification With Different Numbers of Nearest Neighbors
Video Presentation of Data Mining Project 1
 
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Project on Movie ratings and release dates. Used IMDB ratings for 7 years, Spreadsheet, R to cluster.
Views: 1823 jeromejerome19
Effective Query Log Anonymization
 
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Google Tech Talks December 8, 2008 ABSTRACT User search query logs have proven to be very useful, but have vast potential for misuse. Several incidents have shown that simple removal of identifiers is insufficient to protect the identity of users. Publishing such inadequately anonymized data can cause severe breach of privacy. While significant effort has been expended on coming up with anonymity models and techniques for microdata/relational data, there is little corresponding work for query log data -- which is different in several important aspects. In this work, we take a first cut at tackling this problem. Our main contribution is to define effective anonymization models for query log data, along with techniques to achieve such anonymization. Speaker: Dr. Jaideep Vaidya Dr. Jaideep Vaidya is an Assistant Professor at Rutgers University. He received his Masters and Ph.D. at Purdue University and his Bachelors degree at the University of Mumbai. His research interests are in Data Mining, Privacy, Security, and Information Sharing. He has published over 30 papers in international conferences and archival journals, and has received two best paper awards from the premier conferences in data mining and databases. He is also the recipient of a NSF Career Award and is a member of the ACM, and the IEEE Computer Society.
Views: 2071 GoogleTechTalks
Decision Tree Based Classification
 
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Exp 2 in Data Mining Lab
Views: 57 HEMA M UR13CS054
Data Mining Project
 
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Data Mining Project
2013-02-20 CERIAS - Minimizing Private Data Disclosures in the Smart Grid
 
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Recorded: 02/20/2013 CERIAS Security Seminar at Purdue University Minimizing Private Data Disclosures in the Smart Grid Weining Yang, Purdue University Smart electric meters are meters that can measure electric usage with a pretty high frequency. Smart electric meters pose a substantial threat to the privacy of individuals in their own homes. Combined with a method called non-intrusive load monitors, smart meter data can reveal precise home appliance usage information. An emerging solution to behavior leakage in smart meter measurement data is the use of battery-based load hiding. In this approach, a battery is used to store and supply power to home devices at strategic times to hide appliance loads from smart meters. A few such battery control algorithms have already been studied in the literature.In this talk, we will ?rst consider two well known battery privacy algorithms, Best Effort (BE) and Non-Intrusive Load Leveling (NILL), and demonstrate attacks that recover precise load change information, which can be used to recover appliance behavior information, under both algorithms. We will then introduce a stepping approach to battery privacy algorithms that fundamentally differs from previous approaches by maximizing the error between the load demanded by a home and the external load seen by a smart meter. By design, precise load change recovery attacks are impossible. We also propose mutual-information based measurements to evaluate the privacy of different algorithms. We implement and evaluate four novel algorithms using the stepping approach, and show that under the mutual-information metrics they outperform BE and NILL Weining Yang is a PhD student in the Computer Science department of Purdue University. He received his Bachelor's degree in Computer Science and Technology in 2011 from Tsinghua University. His research interests are information security and data privacy. In particular, his research focuses on privacy preserving data publishing. His research advisor is Prof. Ninghui Li. (Visit: www.cerias.purude.edu)
Views: 343 ceriaspurdue
“The Automatic Statistician”– Professor Zoubin Ghahramani
 
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Talk given by Professor of Information Engineering at the University of Cambridge, leader of the Cambridge Machine Learning Group, and the Cambridge Liaison Director of the Alan Turing Institute; Zoubin Ghahramani. The lecture regards the use of Bayesian model selection strategies that automatically select and use models to generate human readable reports. #TuringSeminars
IMPROVING PRIVACY PRESERVING AND SECURITY FOR DECENTRALIZED KEY POLICY ATTRIBUTED BASED ENCRYPTION
 
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IMPROVING PRIVACY PRESERVING AND SECURITY FOR DECENTRALIZED KEY POLICY ATTRIBUTED BASED ENCRYPTION- IEEE PROJECTS 2018 Download projects @ www.micansinfotech.com WWW.SOFTWAREPROJECTSCODE.COM https://www.facebook.com/MICANSPROJECTS Call: +91 90036 28940 ; +91 94435 11725 IEEE PROJECTS, IEEE PROJECTS IN CHENNAI,IEEE PROJECTS IN PONDICHERRY.IEEE PROJECTS 2018,IEEE PAPERS,IEEE PROJECT CODE,FINAL YEAR PROJECTS,ENGINEERING PROJECTS,PHP PROJECTS,PYTHON PROJECTS,NS2 PROJECTS,JAVA PROJECTS,DOT NET PROJECTS,IEEE PROJECTS TAMBARAM,HADOOP PROJECTS,BIG DATA PROJECTS,Signal processing,circuits system for video technology,cybernetics system,information forensic and security,remote sensing,fuzzy and intelligent system,parallel and distributed system,biomedical and health informatics,medical image processing,CLOUD COMPUTING, NETWORK AND SERVICE MANAGEMENT,SOFTWARE ENGINEERING,DATA MINING,NETWORKING ,SECURE COMPUTING,CYBERSECURITY,MOBILE COMPUTING, NETWORK SECURITY,INTELLIGENT TRANSPORTATION SYSTEMS,NEURAL NETWORK,INFORMATION AND SECURITY SYSTEM,INFORMATION FORENSICS AND SECURITY,NETWORK,SOCIAL NETWORK,BIG DATA,CONSUMER ELECTRONICS,INDUSTRIAL ELECTRONICS,PARALLEL AND DISTRIBUTED SYSTEMS,COMPUTER-BASED MEDICAL SYSTEMS (CBMS),PATTERN ANALYSIS AND MACHINE INTELLIGENCE,SOFTWARE ENGINEERING,COMPUTER GRAPHICS, INFORMATION AND COMMUNICATION SYSTEM,SERVICES COMPUTING,INTERNET OF THINGS JOURNAL,MULTIMEDIA,WIRELESS COMMUNICATIONS,IMAGE PROCESSING,IEEE SYSTEMS JOURNAL,CYBER-PHYSICAL-SOCIAL COMPUTING AND NETWORKING,DIGITAL FORENSIC,DEPENDABLE AND SECURE COMPUTING,AI - MACHINE LEARNING (ML),AI - DEEP LEARNING ,AI - NATURAL LANGUAGE PROCESSING ( NLP ),AI - VISION (IMAGE PROCESSING),mca project BIG DATA 1. A Meta Path based Method for Entity Set Expansion in Knowledge Graph 2. Towards Green Cloud Computing: Demand Allocation and Pricing Policies for Cloud Service Brokerage 3. Security-Aware Resource Allocation for Mobile Social Big Data: A Matching Coalitional Game Solution 4. Revocable Identity-Based Access Control for Big Data with Verifiable Outsourced Computing 5. An Efficient and Fine-Grained Big Data Access Control Scheme With Privacy-Preserving Policy 6. HDM:A Compostable Framework for Big Data Processing 7. Dip-SVM : Distribution Preserving KernelSupport Vector Machine for Big Data 8. A Secure and Verifiable Access Control Scheme for Big Data Storage in Cloud 9. Game Theory Based Correlated Privacy Preserving Analysis in Big Data 10. Secure Authentication in Cloud Big Data with Hierarchical Attribute Authorization Structure 11. System to Recommend the Best Place to Live Based on Wellness State of the User Employing 12. Efficient Top-k Dominating Computation on Massive Data 13. Big Data Based Security Analytics for Protecting Virtualized Infrastructures in Cloud Computing 14. Disease Prediction by Machine Learning over Big Data from Healthcare Communities 15. Machine Learning with Big Data: Challenges and Approaches 16. Analyzing Healthcare Big Data with Predictionfor Future Health Condition 17. Robust Big Data Analytics for Electricity Price Forecasting in the Smart Grid 18. iShuffle: Improving Hadoop Performance with Shuffle-on-Write 19. Optimizing Share Size in Efficient and Robust Secret Sharing Scheme 20. Big data privacy in Biomedical research 21. Mining Human Activity Patterns From Smart Home Big Data for Health Care Applications 22. STaRS: Simulating Taxi Ride Sharing at Scale 23. Modeling Urban Behavior by Mining Geotagged Social Data 24. Apriori Versions Based on MapReduce for Mining Frequent Patterns on Big Data 25. Managing Big data using Hadoop Map Reduce in Telecom Domain 26. A Security Model for Preserving the Privacy of Medical Big Data in a Healthcare Cloud Using a Fog Computing Facility with Pairing-Based Cryptography 27. Mutual Privacy Preservingk-Means Clustering in Social Participatory Sensing 28. Measuring Scale-Up and Scale-Out Hadoop with Remote and Local File Systems and Selecting the Best Platform 29. Efficient Recommendation of De-identification Policies using MapReduce CONSUMER ELECTRONICS,INDUSTRIAL ELECTRONICS 1. RRPhish Anti-Phishing via Mining Brand Resources Request 2. Confidence-interval Fuzzy Model-based Indoor Localization COMPUTER-BASED MEDICAL SYSTEMS (CBMS) 1. Population Health Management exploiting Machine Learning Algorithms to identify High-Risk Patients (23 July 2018) PATTERN ANALYSIS AND MACHINE INTELLIGENCE 1. Trunk-Branch Ensemble Convolutional Neural Networks for Video-based Face Recognition ( April 1 2018 ) 2. Detecting Regions of Maximal Divergence for Spatio-Temporal Anomaly Detection 3. Ordinal Constraint Binary Coding for Approximate Nearest Neighbor Search
An integrated optimization system for safe job assignment based on human factors and behavior
 
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An integrated optimization system for safe job assignment based on human factors and behavior- IEEE PROJECTS 2018 Download projects @ www.micansinfotech.com WWW.SOFTWAREPROJECTSCODE.COM https://www.facebook.com/MICANSPROJECTS Call: +91 90036 28940 ; +91 94435 11725 IEEE PROJECTS, IEEE PROJECTS IN CHENNAI,IEEE PROJECTS IN PONDICHERRY.IEEE PROJECTS 2018,IEEE PAPERS,IEEE PROJECT CODE,FINAL YEAR PROJECTS,ENGINEERING PROJECTS,PHP PROJECTS,PYTHON PROJECTS,NS2 PROJECTS,JAVA PROJECTS,DOT NET PROJECTS,IEEE PROJECTS TAMBARAM,HADOOP PROJECTS,BIG DATA PROJECTS,Signal processing,circuits system for video technology,cybernetics system,information forensic and security,remote sensing,fuzzy and intelligent system,parallel and distributed system,biomedical and health informatics,medical image processing,CLOUD COMPUTING, NETWORK AND SERVICE MANAGEMENT,SOFTWARE ENGINEERING,DATA MINING,NETWORKING ,SECURE COMPUTING,CYBERSECURITY,MOBILE COMPUTING, NETWORK SECURITY,INTELLIGENT TRANSPORTATION SYSTEMS,NEURAL NETWORK,INFORMATION AND SECURITY SYSTEM,INFORMATION FORENSICS AND SECURITY,NETWORK,SOCIAL NETWORK,BIG DATA,CONSUMER ELECTRONICS,INDUSTRIAL ELECTRONICS,PARALLEL AND DISTRIBUTED SYSTEMS,COMPUTER-BASED MEDICAL SYSTEMS (CBMS),PATTERN ANALYSIS AND MACHINE INTELLIGENCE,SOFTWARE ENGINEERING,COMPUTER GRAPHICS, INFORMATION AND COMMUNICATION SYSTEM,SERVICES COMPUTING,INTERNET OF THINGS JOURNAL,MULTIMEDIA,WIRELESS COMMUNICATIONS,IMAGE PROCESSING,IEEE SYSTEMS JOURNAL,CYBER-PHYSICAL-SOCIAL COMPUTING AND NETWORKING,DIGITAL FORENSIC,DEPENDABLE AND SECURE COMPUTING,AI - MACHINE LEARNING (ML),AI - DEEP LEARNING ,AI - NATURAL LANGUAGE PROCESSING ( NLP ),AI - VISION (IMAGE PROCESSING),mca project CYBER-PHYSICAL-SOCIAL COMPUTING AND NETWORKING 1. Reliable Decision Making of Accepting friend request on social networks (March 13, 2018) 2. Motivating Content Sharing and Trustworthiness in Mobile Social Networks (08 May 2018) 3. Identifying Product Opportunities Using Social Media Mining: Application of Topic Modeling and Chance Discovery Theory ( 06 December 2017) DIGITAL FORENSIC 1. A proposed approach for preventing cross-site scripting (07 May 2018) DEPENDABLE AND SECURE COMPUTING 1. Enhanced Secure Thresholded Data Deduplication Scheme for Cloud Storage ( July-Aug. 1 2018) 2. Deep Learning of Constrained Autoencoders for Enhanced Understanding of Data 3. Efficient Fine-Grained Data Sharing Mechanism for Electronic Medical Record Systems with Mobile Devices 4. Analyzing and Detecting Money-Laundering Accounts in Online Social Networks 5. Magic Train: Design of Measurement Methods Against Bandwidth Inflation Attacks 6. Semantic-based Compound Keyword Search over Encrypted Cloud Data 7. Quality and Profit Assured Trusted Cloud Federation Formation: Game Theory Based Approach 8. Optimizing Autonomic Resources for the Management of Large Service-Based Business Processes 9. Scheduling Inter-Datacenter Video Flows for Cost Efficiency 10. Lightweight Fine-Grained Search over Encrypted Data in Fog Computing 11. SEPDP: Secure and Efficient Privacy Preserving Provable Data Possession in Cloud Storage 12. Migration Modeling and Learning Algorithms for Containers in Fog Computing 13. Multi-user Multi-task Computation Offloading in Green Mobile Edge Cloud Computing 14. Achieving Fairness-aware Two-level Scheduling for Heterogeneous Distributed Systems 15. Shared Predictive Cross-Modal Deep Quantization 16. Fuzzy Identity-Based Data Integrity Auditing for Reliable Cloud Storage Systems 17. Efficient and Private Scoring of Decision Trees, Support Vector Machines and Logistic Regression Models based on Pre-Computation 18. FastGeo: Efficient Geometric Range Queries on Encrypted Spatial Data 19. Cryptographic Solutions for Credibility and Liability Issues of Genomic Data 20. Privacy-Preserving Aggregate Queries for Optimal Location Selection 21. Shadow Attacks based on Password Reuses: A Quantitative Empirical Analysis 22. Wormhole: The Hidden Virus Propagation Power of a Search Engine in Social Networks
Adversarial Attacks on Neural Networks for Graph Data
 
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Authors: Daniel Zügner (Technical University of Munich); Amir Akbarnejad (Technical University of Munich); Stephan Günnemann (Technical University of Munich) Abstract: Deep learning models for graphs have achieved strong performance for the task of node classification. Despite their proliferation, currently there is no study of their robustness to adversarial attacks. Yet, in domains where they are likely to be used, e.g. the web, adversaries are common. Can deep learning models for graphs be easily fooled? In this work, we introduce the first study of adversarial attacks on attributed graphs, specifically focusing on models exploiting ideas of graph convolutions. In addition to attacks at test time, we tackle the more challenging class of poisoning/causative attacks, which focus on the training phase of a machine learning model. We generate adversarial perturbations targeting the node’s features and the graph structure, thus, taking the dependencies between instances in account. Moreover, we ensure that the perturbations remain unnoticeable by preserving important data characteristics. To cope with the underlying discrete domain we propose an efficient algorithm Nettack exploiting incremental computations. Our experimental study shows that accuracy of node classification significantly drops even when performing only few perturbations. Even more, our attacks are transferable: the learned attacks generalize to other state-of-the-art node classification models and unsupervised approaches, and likewise are successful even when only limited knowledge about the graph is given. More on http://www.kdd.org/kdd2018/
Views: 734 KDD2018 video
A Novel Machine Learning Algorithm for Spammer Identification in Industrial Mobile Cloud Computing
 
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2018 IEEE Transaction on Machine Learning For More Details::Contact::K.Manjunath - 09535866270 http://www.tmksinfotech.com and http://www.bemtechprojects.com 2018 and 2019 IEEE [email protected] TMKS Infotech,Bangalore
Views: 436 manju nath
Leakage in Data Mining Competitions and Real Life Projects
 
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Data Mining, from Theory to Practice, Lecture of Prof. Saharon Rosset, School of Mathematical Sciences, Tel-Aviv University, "Leakage in Data Mining Competitions and Real Life Projects" Data Mining for Business Intelligence - Bridging the Gap Ben-Gurion University of the Negev
Views: 401 BenGurionUniversity
Class-distribution regularized consensus maximization for alleviating.. (KDD 2014 Presentation)
 
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Class-distribution regularized consensus maximization for alleviating overfitting in model combination KDD 2014 Presentation Sihong Xie Jing Gao Wei Fan Deepak Turaga Philip S. Yu In data mining applications such as crowdsourcing and privacy-preserving data mining, one may wish to obtain consolidated predictions out of multiple models without access to features of the data. Besides, multiple models usually carry complementary predictive information, model combination can potentially provide more robust and accurate predictions by correcting independent errors from individual models. Various methods have been proposed to combine predictions such that the final predictions are maximally agreed upon by multiple base models. Though this maximum consensus principle has been shown to be successful, simply maximizing consensus can lead to less discriminative predictions and overfit the inevitable noise due to imperfect base models. We argue that proper regularization for model combination approaches is needed to alleviate such overfitting effect. Specifically, we analyze the hypothesis spaces of several model combination methods and identify the trade-off between model consensus and generalization ability. We propose a novel model called Regularized Consensus Maximization (RCM), which is formulated as an optimization problem to combine the maximum consensus and large margin principles. We theoretically show that RCM has a smaller upper bound on generalization error compared to the version without regularization. Experiments show that the proposed algorithm outperforms a wide spectrum of state-of-the-art model combination methods on 11 tasks.
Enabling identity-based integrity auditing and data sharing with sensitive information hiding
 
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Enabling identity-based integrity auditing and data sharing with sensitive information hiding for secure cloud storage - IEEE PROJECTS 2018 Download projects @ www.micansinfotech.com WWW.SOFTWAREPROJECTSCODE.COM https://www.facebook.com/MICANSPROJECTS Call: +91 90036 28940 ; +91 94435 11725 IEEE PROJECTS, IEEE PROJECTS IN CHENNAI,IEEE PROJECTS IN PONDICHERRY.IEEE PROJECTS 2018,IEEE PAPERS,IEEE PROJECT CODE,FINAL YEAR PROJECTS,ENGINEERING PROJECTS,PHP PROJECTS,PYTHON PROJECTS,NS2 PROJECTS,JAVA PROJECTS,DOT NET PROJECTS,IEEE PROJECTS TAMBARAM,HADOOP PROJECTS,BIG DATA PROJECTS,Signal processing,circuits system for video technology,cybernetics system,information forensic and security,remote sensing,fuzzy and intelligent system,parallel and distributed system,biomedical and health informatics,medical image processing,CLOUD COMPUTING, NETWORK AND SERVICE MANAGEMENT,SOFTWARE ENGINEERING,DATA MINING,NETWORKING ,SECURE COMPUTING,CYBERSECURITY,MOBILE COMPUTING, NETWORK SECURITY,INTELLIGENT TRANSPORTATION SYSTEMS,NEURAL NETWORK,INFORMATION AND SECURITY SYSTEM,INFORMATION FORENSICS AND SECURITY,NETWORK,SOCIAL NETWORK,BIG DATA,CONSUMER ELECTRONICS,INDUSTRIAL ELECTRONICS,PARALLEL AND DISTRIBUTED SYSTEMS,COMPUTER-BASED MEDICAL SYSTEMS (CBMS),PATTERN ANALYSIS AND MACHINE INTELLIGENCE,SOFTWARE ENGINEERING,COMPUTER GRAPHICS, INFORMATION AND COMMUNICATION SYSTEM,SERVICES COMPUTING,INTERNET OF THINGS JOURNAL,MULTIMEDIA,WIRELESS COMMUNICATIONS,IMAGE PROCESSING,IEEE SYSTEMS JOURNAL,CYBER-PHYSICAL-SOCIAL COMPUTING AND NETWORKING,DIGITAL FORENSIC,DEPENDABLE AND SECURE COMPUTING,AI - MACHINE LEARNING (ML),AI - DEEP LEARNING ,AI - NATURAL LANGUAGE PROCESSING ( NLP ),AI - VISION (IMAGE PROCESSING),mca project PARALLEL AND DISTRIBUTED SYSTEMS 1. Enhancing Collusion Resilience in Reputation Systems 2. A Crowdsourcing Worker Quality Evaluation Algorithm on MapReduce for Big Data Applications 3. Evaluating Replication for Parallel Jobs: An Efficient Approach 4. Conditions and Patterns for Achieving Convergence in OT-Based Co-Editors 5. Prefetching on Storage Servers through Mining Access Patterns on Blocks 6. SPA: A Secure and Private Auction Framework for Decentralized Online Social Networks 7. Predicting Cross-Core Performance Interference on Multicore Processors with Regression Analysis 8. Collaboration- and Fairness-Aware Big Data Management in Distributed Clouds 9. RFHOC: A Random-Forest Approach to Auto-Tuning Hadoop's Configuration 10. Deadline Guaranteed Service for Multi-Tenant Cloud Storage 11. Carbon-Aware Online Control of Geo-Distributed Cloud Services 12. Online Resource Scheduling Under Concave Pricing for Cloud Computing 13. Burstiness-Aware Resource Reservation for Server Consolidation in Computing Clouds 14. Performance Evaluation of Cloud Computing Centers with General Arrivals and Service 15. TMACS: A Robust and Verifiable Threshold Multi-Authority Access Control System in Public Cloud Storage 16. Heads-Join: Efficient Earth Mover's Distance Similarity Joins on Hadoop 17. Enabling Personalized Search over Encrypted Outsourced Data with Efficiency Improvement 18. Quantum-Inspired Hyper-Heuristics for Energy-Aware Scheduling on Heterogeneous Computing Systems 19. A Secure and Dynamic Multi-Keyword Ranked Search Scheme over Encrypted Cloud Data 20. A High Performance Parallel and Heterogeneous Approach to Narrowband Beamforming 21. EcoUp: Towards Economical Datacenter Upgrading 22. Hadoop Performance Modeling for Job Estimation and Resource Provisioning 23. Optimization of the Processing of Data Streams on Roughly Characterized Distributed Resources 24. A Secure Anti-Collusion Data Sharing Scheme for Dynamic Groups in the Cloud 25. Exploring Heterogeneity within a Core for Improved Power Efficiency 26. Efficient File Search in Delay Tolerant Networks with Social Content and Contact Awareness 27. Exploiting Workload Characteristics and Service Diversity to Improve the Availability of Cloud Storage Systems 28. PerfCompass: Online Performance Anomaly Fault Localization and Inference in Infrastructure-as-a-Service Clouds 29. Energy and Makespan Tradeoffs in Heterogeneous Computing Systems using Efficient Linear Programming Techniques 30. An Efficient Privacy-Preserving Ranked Keyword Search Method 31. GrapH: Traffic-Aware Graph Processing (June 1 2018) 32. Automatic construction of vertical search tools for the Deep Web (Feb. 2018) 33. Towards Long-View Computing Load Balancing in Cluster Storage Systems 34. ATOM: Efficient Tracking, Monitoring, and Orchestration of Cloud Resources 35. Stochastic Resource Provisioning for Containerized Multi-Tier Web Services in Clouds 36. Repair Tree: Fast Repair for Single Failure in Erasure-coded Distributed Storage Systems 37. A Load Balancing and Multi-tenancy Oriented Data Center Virtualization Framework 38. Stochastic Resource Provisioning for Containerized Multi-Tier Web Services in Clouds
CERIAS Security: PrivacyEnhancing k-Anonymization of Customer Data 1/9
 
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Clip 1/9 Speaker: Sheng Zhong · SUNY at Buffalo In order to protect individuals' privacy, the technique of k-anonymization has been proposed to de-associate sensitive attributes from the corresponding identifiers. In this work, we provide privacy-enhancing methods for creating k-anonymous tables in a distributed scenario. Specifically, we consider a setting in which there is a set of customers, each of whom has a row of a table, and a miner, who wants to mine the en- tire table. Our objective is to design protocols that allow the miner to obtain a k-anonymous table representing the customer data, in such a way that does not reveal any extra information that can be used to link sensitive attributes to corresponding identifiers, and without requiring a central authority who has access to all the original data. We give two different formulations of this problem, with provably private solutions. Our solutions enhance the privacy of k-anonymization in the distributed scenario by maintaining end-to-end privacy from the original customer data to the final k-anonymous results. For more information go to the Cerias website (http://bit.ly/dsFCBF)
Views: 2083 Christiaan008
Automatic construction of vertical search tools for the deep web- IEEE PROJECTS 2018
 
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Automatic construction of vertical search tools for the deep web- IEEE PROJECTS 2018 Download projects @ www.micansinfotech.com WWW.SOFTWAREPROJECTSCODE.COM https://www.facebook.com/MICANSPROJECTS Call: +91 90036 28940 ; +91 94435 11725 IEEE PROJECTS, IEEE PROJECTS IN CHENNAI,IEEE PROJECTS IN PONDICHERRY.IEEE PROJECTS 2018,IEEE PAPERS,IEEE PROJECT CODE,FINAL YEAR PROJECTS,ENGINEERING PROJECTS,PHP PROJECTS,PYTHON PROJECTS,NS2 PROJECTS,JAVA PROJECTS,DOT NET PROJECTS,IEEE PROJECTS TAMBARAM,HADOOP PROJECTS,BIG DATA PROJECTS,Signal processing,circuits system for video technology,cybernetics system,information forensic and security,remote sensing,fuzzy and intelligent system,parallel and distributed system,biomedical and health informatics,medical image processing,CLOUD COMPUTING, NETWORK AND SERVICE MANAGEMENT,SOFTWARE ENGINEERING,DATA MINING,NETWORKING ,SECURE COMPUTING,CYBERSECURITY,MOBILE COMPUTING, NETWORK SECURITY,INTELLIGENT TRANSPORTATION SYSTEMS,NEURAL NETWORK,INFORMATION AND SECURITY SYSTEM,INFORMATION FORENSICS AND SECURITY,NETWORK,SOCIAL NETWORK,BIG DATA,CONSUMER ELECTRONICS,INDUSTRIAL ELECTRONICS,PARALLEL AND DISTRIBUTED SYSTEMS,COMPUTER-BASED MEDICAL SYSTEMS (CBMS),PATTERN ANALYSIS AND MACHINE INTELLIGENCE,SOFTWARE ENGINEERING,COMPUTER GRAPHICS, INFORMATION AND COMMUNICATION SYSTEM,SERVICES COMPUTING,INTERNET OF THINGS JOURNAL,MULTIMEDIA,WIRELESS COMMUNICATIONS,IMAGE PROCESSING,IEEE SYSTEMS JOURNAL,CYBER-PHYSICAL-SOCIAL COMPUTING AND NETWORKING,DIGITAL FORENSIC,DEPENDABLE AND SECURE COMPUTING,AI - MACHINE LEARNING (ML),AI - DEEP LEARNING ,AI - NATURAL LANGUAGE PROCESSING ( NLP ),AI - VISION (IMAGE PROCESSING),mca project INFORMATION AND COMMUNICATION SYSTEM 1. A Data Mining based Model for Detection of Fraudulent Behaviour in Water Consumption SERVICES COMPUTING 1. SVM-DT-Based Adaptive and Collaborative Intrusion Detection (jan 2018) 2. Cloud Workflow Scheduling With Deadlines And Time Slot Availability (March-April 1 2018) 3. Secure and Sustainable Load Balancing of Edge Data Centers in Fog Computing (17 May 2018) 4. Semantic-based Compound Keyword Search over Encrypted Cloud Data 5. Quality and Profit Assured Trusted Cloud Federation Formation: Game Theory Based Approach 6. Optimizing Autonomic Resources for the Management of Large Service-Based Business Processes 7. Scheduling Inter-Datacenter Video Flows for Cost Efficiency 8. Lightweight Fine-Grained Search over Encrypted Data in Fog Computing 9. SEPDP: Secure and Efficient Privacy Preserving Provable Data Possession in Cloud Storage 10. Migration Modeling and Learning Algorithms for Containers in Fog Computing 11. Multi-user Multi-task Computation Offloading in Green Mobile Edge Cloud Computing 12. Achieving Fairness-aware Two-level Scheduling for Heterogeneous Distributed Systems INTERNET OF THINGS JOURNAL 1. SMDP-Based Coordinated Virtual Machine Allocations in Cloud-Fog Computing Systems (June 2018) 2. Mobile Data Gathering with Bounded Relay in Wireless Sensor Networks(06 June 2018) 3. Robot Assistant in Management of Diabetes in Children Based on the Internet of Things 4. Secure and Efficient Protocol for Route Optimization in PMIPv6-based Smart Home IOT Networks 5. A Novel Internet of Things-centric Framework to Mine Malicious Frequent Patterns 6. Mobile Cloud-Based Big Healthcare Data Processing in Smart Cities 7. Achieving Efficient and Secure DataAcquisition for Cloud-supported Internet of Things in Smart Grid 8. Real-Time Signal Quality-Aware ECG Telemetry System for IoT-Based Health Care Monitoring 9. A Privacy Preserving Communication Protocol for IoT Applications in Smart Homes 10. Softwarization of Internet of Things Infrastructure for Secure and Smart Healthcare MULTIMEDIA 1. A Personalized Group-Based Recommendation Approach for Web Search in E-Learning ( 25 June 2018) 2. A Unified Framework for Tracking Based Text Detection and Recognition from Web Videos 3. Automatic Annotation of Text with Pictures 4. Joint Latent Dirichlet Allocation for Social Tags WIRELESS COMMUNICATIONS 1. Energy efficient modelling of a network(12 February 2018) 2. A new autonomous data transmission reduction method for wireless sensors networks 3. Securing Ad-hoc On-Demand Distance Vector Protocol in Wireless Sensor Networks (07 June 2018) IMAGE PROCESSING 1. Two reversible data hiding schemes for VQ-compressed images based on index coding (18 June 2018) 2. Image Re-ranking based on Topic Diversity 3. A Novel Data Hiding Algorithm for High Dynamic Range Images 4. Practical Privacy-Preserving Content-Based Retrieval in Cloud Image Repositories IEEE SYSTEMS JOURNAL 1. FTP-NDN File Transfer Protocol Based on Re-Encryption for Named Data Network Supporting Nondesignated Receivers(March 2018 ) 2. An Integrated Optimization System for Safe Job Assignment Based on Human Factors and Behavior (June 2018)
Privacy Preserving Delegated Access Control in Public Clouds | IEEE 2013
 
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To get this project in ONLINE or through TRAINING Sessions, Contact: JP INFOTECH, 45, KAMARAJ SALAI, THATTANCHAVADY, PUDUCHERRY-9 Landmark: Opposite to Thattanchavady Industrial Estate, Next to VVP Nagar Arch. Mobile: (0) 9952649690 , Email: [email protected], web: www.jpinfotech.org Blog: www.jpinfotech.blogspot.com Privacy Preserving Delegated Access Control in Public Clouds | IEEE 2013 Current approaches to enforce fine-grained access control on confidential data hosted in the cloud are based on fine-grained encryption of the data. Under such approaches, data owners are in charge of encrypting the data before uploading them on the cloud and re-encrypting the data whenever user credentials change. Data owners thus incur high communication and computation costs. A better approach should delegate the enforcement offline-grained access control to the cloud, so to minimize the overhead at the data owners, while assuring data confidentiality from the cloud. We propose an approach, based on two layers of encryption that addresses such requirement. Under our approach, the data owner performs a coarse-grained encryption, whereas the cloud performs a fine-grained encryption on top of the owner encrypted data. A challenging issue is how to decompose access control policies (ACPs) such that the two layer encryption can be performed. We show that this problem is NP-complete and propose novel optimization algorithms. We utilize an efficient group key management scheme that supports expressive ACPs. Our system assures the confidentiality of the data and preserves the privacy of users from the cloud while delegating most of the access control enforcement to the cloud.
Views: 728 jpinfotechprojects
SECURE AND SUSTAINABLE LOAD BALANCING OF EDGE DATA CENTERS IN FOG COMPUTING- IEEE PROJECTS 2018
 
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SECURE AND SUSTAINABLE LOAD BALANCING OF EDGE DATA CENTERS IN FOG COMPUTING- IEEE PROJECTS 2018 Download projects @ www.micansinfotech.com WWW.SOFTWAREPROJECTSCODE.COM https://www.facebook.com/MICANSPROJECTS Call: +91 90036 28940 ; +91 94435 11725 IEEE PROJECTS, IEEE PROJECTS IN CHENNAI,IEEE PROJECTS IN PONDICHERRY.IEEE PROJECTS 2018,IEEE PAPERS,IEEE PROJECT CODE,FINAL YEAR PROJECTS,ENGINEERING PROJECTS,PHP PROJECTS,PYTHON PROJECTS,NS2 PROJECTS,JAVA PROJECTS,DOT NET PROJECTS,IEEE PROJECTS TAMBARAM,HADOOP PROJECTS,BIG DATA PROJECTS,Signal processing,circuits system for video technology,cybernetics system,information forensic and security,remote sensing,fuzzy and intelligent system,parallel and distributed system,biomedical and health informatics,medical image processing,CLOUD COMPUTING, NETWORK AND SERVICE MANAGEMENT,SOFTWARE ENGINEERING,DATA MINING,NETWORKING ,SECURE COMPUTING,CYBERSECURITY,MOBILE COMPUTING, NETWORK SECURITY,INTELLIGENT TRANSPORTATION SYSTEMS,NEURAL NETWORK,INFORMATION AND SECURITY SYSTEM,INFORMATION FORENSICS AND SECURITY,NETWORK,SOCIAL NETWORK,BIG DATA,CONSUMER ELECTRONICS,INDUSTRIAL ELECTRONICS,PARALLEL AND DISTRIBUTED SYSTEMS,COMPUTER-BASED MEDICAL SYSTEMS (CBMS),PATTERN ANALYSIS AND MACHINE INTELLIGENCE,SOFTWARE ENGINEERING,COMPUTER GRAPHICS, INFORMATION AND COMMUNICATION SYSTEM,SERVICES COMPUTING,INTERNET OF THINGS JOURNAL,MULTIMEDIA,WIRELESS COMMUNICATIONS,IMAGE PROCESSING,IEEE SYSTEMS JOURNAL,CYBER-PHYSICAL-SOCIAL COMPUTING AND NETWORKING,DIGITAL FORENSIC,DEPENDABLE AND SECURE COMPUTING,AI - MACHINE LEARNING (ML),AI - DEEP LEARNING ,AI - NATURAL LANGUAGE PROCESSING ( NLP ),AI - VISION (IMAGE PROCESSING),mca project INTERNET OF THINGS JOURNAL 1. SMDP-Based Coordinated Virtual Machine Allocations in Cloud-Fog Computing Systems (June 2018) 2. Mobile Data Gathering with Bounded Relay in Wireless Sensor Networks(06 June 2018) 3. Robot Assistant in Management of Diabetes in Children Based on the Internet of Things 4. Secure and Efficient Protocol for Route Optimization in PMIPv6-based Smart Home IOT Networks 5. A Novel Internet of Things-centric Framework to Mine Malicious Frequent Patterns 6. Mobile Cloud-Based Big Healthcare Data Processing in Smart Cities 7. Achieving Efficient and Secure DataAcquisition for Cloud-supported Internet of Things in Smart Grid 8. Real-Time Signal Quality-Aware ECG Telemetry System for IoT-Based Health Care Monitoring 9. A Privacy Preserving Communication Protocol for IoT Applications in Smart Homes 10. Softwarization of Internet of Things Infrastructure for Secure and Smart Healthcare MULTIMEDIA 1. A Personalized Group-Based Recommendation Approach for Web Search in E-Learning ( 25 June 2018) 2. A Unified Framework for Tracking Based Text Detection and Recognition from Web Videos 3. Automatic Annotation of Text with Pictures 4. Joint Latent Dirichlet Allocation for Social Tags WIRELESS COMMUNICATIONS 1. Energy efficient modelling of a network(12 February 2018) 2. A new autonomous data transmission reduction method for wireless sensors networks 3. Securing Ad-hoc On-Demand Distance Vector Protocol in Wireless Sensor Networks (07 June 2018) IMAGE PROCESSING 1. Two reversible data hiding schemes for VQ-compressed images based on index coding (18 June 2018) 2. Image Re-ranking based on Topic Diversity 3. A Novel Data Hiding Algorithm for High Dynamic Range Images 4. Practical Privacy-Preserving Content-Based Retrieval in Cloud Image Repositories IEEE SYSTEMS JOURNAL 1. FTP-NDN File Transfer Protocol Based on Re-Encryption for Named Data Network Supporting Nondesignated Receivers(March 2018 ) 2. An Integrated Optimization System for Safe Job Assignment Based on Human Factors and Behavior (June 2018) CYBER-PHYSICAL-SOCIAL COMPUTING AND NETWORKING 1. Reliable Decision Making of Accepting friend request on social networks (March 13, 2018) 2. Motivating Content Sharing and Trustworthiness in Mobile Social Networks (08 May 2018) 3. Identifying Product Opportunities Using Social Media Mining: Application of Topic Modeling and Chance Discovery Theory ( 06 December 2017) DIGITAL FORENSIC 1. A proposed approach for preventing cross-site scripting (07 May 2018) DEPENDABLE AND SECURE COMPUTING 1. Enhanced Secure Thresholded Data Deduplication Scheme for Cloud Storage ( July-Aug. 1 2018) 2. Deep Learning of Constrained Autoencoders for Enhanced Understanding of Data 3. Efficient Fine-Grained Data Sharing Mechanism for Electronic Medical Record Systems with Mobile Devices 4. Analyzing and Detecting Money-Laundering Accounts in Online Social Networks
Mining Group Movement Patterns for Tracking Moving Objects Efficiently
 
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To get this project in ONLINE or through TRAINING Sessions, Contact: JP INFOTECH, 45, KAMARAJ SALAI, THATTANCHAVADY, PUDUCHERRY-9 Landmark: Opposite to Thattanchavady Industrial Estate, Next to VVP Nagar Arch. Mobile: (0) 9952649690 , Email: [email protected], web: www.jpinfotech.org Blog: www.jpinfotech.blogspot.com Mining Group Movement Patterns for Tracking Moving Objects Efficiently Existing object tracking applications focus on finding the moving patterns of a single object or all objects. In contrast, we propose a distributed mining algorithm that identifies a group of objects with similar movement patterns. This information is important in some biological research domains, such as the study of animals' social behavior and wildlife migration. The proposed algorithm comprises a local mining phase and a cluster ensembling phase. In the local mining phase, the algorithm finds movement patterns based on local trajectories. Then, based on the derived patterns, we propose a new similarity measure to compute the similarity of moving objects and identify the local group relationships. To address the energy conservation issue in resource-constrained environments, the algorithm only transmits the local grouping results to the sink node for further ensembling. In the cluster ensembling phase, our algorithm combines the local grouping results to derive the group relationships from a global view. We further leverage the mining results to track moving objects efficiently. The results of experiments show that the proposed mining algorithm achieves good grouping quality, and the mining technique helps reduce the energy consumption by reducing the amount of data to be transmitted.
Views: 161 JPINFOTECH PROJECTS
Comparative Study of Decision Tree Algoritms-Major Project 2014
 
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by Team No.28 Suresh Patil Mahesh B Swamy B Tejendra Varma D
Views: 73 suresh patil

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