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Privacy Preserving Data Mining (eng)
 
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Demonstration of a tool developed from I.S.T. SA (http://www.ist.com.gr/) for enabling privacy preserving data mining. See also: * https://goo.gl/JVQ28q * https://goo.gl/xAqixd
Views: 132 Daiquiri IST
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: 3684 GoogleTalksArchive
Privacy Preserving Data Sharing With Anonymous ID Assignment
 
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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 Data Sharing With Anonymous ID Assignment An algorithm for anonymous sharing of private data among parties is developed. This technique is used iteratively to assign these nodes ID numbers ranging from 1 to N. This assignment is anonymous in that the identities received are unknown to the other members of the group. Resistance to collusion among other members is verified in an information theoretic sense when private communication channels are used. This assignment of serial numbers allows more complex data to be shared and has applications to other problems in privacy preserving data mining, collision avoidance in communications and distributed database access. The required computations are distributed without using a trusted central authority. Existing and new algorithms for assigning anonymous IDs are examined with respect to trade-offs between communication and computational requirements. The new algorithms are built on top of a secure sum data mining operation using Newton's identities and Sturm's theorem. An algorithm for distributed solution of certain polynomials over finite fields enhances the scalability of the algorithms. Markov chain representations are used to find statistics on the number of iterations required, and computer algebra gives closed form results for the completion rates.
Views: 998 jpinfotechprojects
How to Preserve Privacy using Data Mining
 
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Every person involved,is concerned about the leakage of private data i.e privacy of the individual's data.Today privacy of data is one of the most serious concerns which people face on an individual as well as organisational level and it has to be dealt with an effective manner using privacy preserving data mining.
A Framework for Categorizing and Applying Privacy-Preservation Techniques in Big Data Mining
 
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A Framework for Categorizing and Applying Privacy-Preservation Techniques in Big Data Mining
Privacy-Preserving Learning Analytics: Challenges and Techniques
 
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Privacy-Preserving Learning Analytics: Challenges and Techniques Project in Java. Download Full Project code, Report, PPT :+91 7702177291, +91 9052016340 Email : [email protected] Website : www.1000projects.org
Views: 342 1000 Projects
Privacy-Preserving Data Compression
 
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Privacy-Preserving Data Compression Lijuan Cui, MS
Views: 101 Calit2ube
Final Year Projects | Enabling Multilevel Trust in Privacy Preserving Data Mining
 
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Final Year Projects | Enabling Multilevel Trust in Privacy Preserving Data Mining More Details: Visit http://clickmyproject.com/a-secure-erasure-codebased-cloud-storage-system-with-secure-data-forwarding-p-128.html Including Packages ======================= * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ http://clickmyproject.com Get Discount @ https://goo.gl/lGybbe Chat Now @ http://goo.gl/snglrO Visit Our Channel: http://www.youtube.com/clickmyproject Mail Us: [email protected]
Views: 1452 Clickmyproject
Privacy-Preserving Data Analysis - Mechanisms and Formal Guarantees (EINS Summer School 2012)
 
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In this lecture, Joss Wright of the Oxford Internet Institute, examines from a technological angle, the problems involved with gathering, storing and accessing data about individuals in databases whilst preserving their individual privacy. Starting with basic computer-science definitions of privacy, this lecture explores early work in reidentifying individuals and their sensitive characteristics in apparently anonymous and anonymised data. It presents early approaches used to prevent such reidentification, the limitations of these methods, and examples of high-profile reidentifications performed on real-world data. Finally the current state of the art, differential privacy, is introduced and explained, along with its strengths, limitations and practical applications.
A Random Decision Tree Framework for Privacy Preserving Data Mining
 
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A Random Decision Tree Framework for Privacy Preserving Data Mining C:+91 8121953811,L:040-65511811 M:[email protected] All 2014 Implementations http://goo.gl/ljRsEI http://cloudstechnologies.in/
Views: 354 Cloud Technologies
Privacy and Security Issues in Big Data and Data Mining
 
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This talk will cover privacy-preserving data mining, an emerging research topic in data mining, whose basic idea is to modify the data in such a way so as to perform data mining algorithms effectively without compromising the security of sensitive information contained in the data. Speaker: Vyas Krishnan, Ph.D. Associate Professor of Computer Science Saint Leo University
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/
Privacy Preserving Data Sharing With Anonymous ID Assignment
 
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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 Data Sharing With Anonymous ID Assignment An algorithm for anonymous sharing of private data among parties is developed. This technique is used iteratively to assign these nodes ID numbers ranging from 1 to N. This assignment is anonymous in that the identities received are unknown to the other members of the group. Resistance to collusion among other members is verified in an information theoretic sense when private communication channels are used. This assignment of serial numbers allows more complex data to be shared and has applications to other problems in privacy preserving data mining, collision avoidance in communications and distributed database access. The required computations are distributed without using a trusted central authority. Existing and new algorithms for assigning anonymous IDs are examined with respect to trade-offs between communication and computational requirements. The new algorithms are built on top of a secure sum data mining operation using Newton's identities and Sturm's theorem. An algorithm for distributed solution of certain polynomials over finite fields enhances the scalability of the algorithms. Markov chain representations are used to find statistics on the number of iterations required, and computer algebra gives closed form results for the completion rates.
Views: 1437 jpinfotechprojects
Privacy Preserving Data Publishing for Multiple Numerical Sensitive Attributes
 
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Views: 206 1 Crore Projects
A Random Decision Tree Framework for Privacy Preserving Data Mining
 
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ChennaiSunday Systems Pvt.Ltd We are ready to provide guidance to successfully complete your projects and also download the abstract, base paper from our website IEEE 2014 Java Projects: http://www.chennaisunday.com/projectsNew.php?id=1&catName=IEEE_2014-2015_Java_Projects IEEE 2014 Dotnet Projects: http://www.chennaisunday.com/projectsNew.php?id=20&catName=IEEE_2014-2015_DotNet_Projects Output Videos: https://www.youtube.com/channel/UCCpF34pmRlZbAsbkareU8_g/videos IEEE 2013 Java Projects: http://www.chennaisunday.com/projectsNew.php?id=2&catName=IEEE_2013-2014_Java_Projects IEEE 2013 Dotnet Projects: http://www.chennaisunday.com/projectsNew.php?id=3&catName=IEEE_2013-2014_Dotnet_Projects Output Videos: https://www.youtube.com/channel/UCpo4sL0gR8MFTOwGBCDqeFQ/videos IEEE 2012 Java Projects: http://www.chennaisunday.com/projectsNew.php?id=26&catName=IEEE_2012-2013_Java_Projects Output Videos: https://www.youtube.com/user/siva6351/videos IEEE 2012 Dotnet Projects: http://www.chennaisunday.com/projectsNew.php?id=28&catName=IEEE_2012-2013_Dotnet_Projects Output Videos: https://www.youtube.com/channel/UC4nV8PIFppB4r2wF5N4ipqA/videos IEEE 2011 Java Projects: http://chennaisunday.com/projectsNew.php?id=29&catName=IEEE_2011-2012_Java_Project IEEE 2011 Dotnet Projects: http://chennaisunday.com/projectsNew.php?id=33&catName=IEEE_2011-2012_Dotnet_Projects Output Videos: https://www.youtube.com/channel/UCtmBGO0q5XZ5UsMW0oDhZ-A/videos IEEE PHP Projects: http://www.chennaisunday.com/projectsNew.php?id=41&catName=IEEE_PHP_Projects Output Videos: https://www.youtube.com/user/siva6351/videos Java Application Projects: http://www.chennaisunday.com/projectsNew.php?id=34&catName=Java_Application_Projects Output Videos: https://www.youtube.com/channel/UCPqHN-x10SazValUi9Konlg/videos Dotnet Application Projects: http://www.chennaisunday.com/projectsNew.php?id=35&catName=Dotnet_Application_Projects Output Videos: https://www.youtube.com/channel/UCMTKwKCCJvpErttDqCuG1jA/videos Android Application Projects: http://www.chennaisunday.com/projectsNew.php?id=36&catName=Android_Application_Projects PHP Application Projects: http://www.chennaisunday.com/projectsNew.php?id=37&catName=PHP_Application_Projects Struts Application Projects: http://www.chennaisunday.com/projectsNew.php?id=38&catName=Struts_Application_Projects Java Mini Projects: http://www.chennaisunday.com/projectsNew.php?id=39&catName=Java_Mini_Projects Dotnet Mini Projects: http://www.chennaisunday.com/projectsNew.php?id=40&catName=Dotnet_Mini_Projects -- *Contact * * P.Sivakumar MCA Director Chennai Sunday Systems Pvt Ltd Phone No: 09566137117 No: 1,15th Street Vel Flats Ashok Nagar Chennai-83 Landmark R3 Police Station Signal (Via 19th Street) URL: www.chennaisunday.com Map View: http://chennaisunday.com/locationmap.php
Views: 606 siva kumar
l diversity k anonymity for privacy preserving data ( Java)
 
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This work is an adaptation of the paper: http://ijarcsee.org/index.php/IJARCSEE/article/view/219/197 The objective is to change the view of a table containing sensitive information such that the table does not loose any significant information and yet can provide a generalized form. This is extremely important from survey point of view and to present such data by ensuring privacy preservation of the people such that under no way a particular person's identity can be obtained from partial information.
Views: 5594 rupam rupam
Slicing: An Approach for Privacy Preservation in High-Dimensional Data Using Anonymization Technique
 
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Data Anonymization is always being a subject of researchers in the last few years. Privacy Preserving Data Mining, i.e. the study of data mining side-effects on privacy, which receives an increasing attention from the research community. Privacy-preservation data publishing has received lot of attention, as it is always a problem of how to secure a database of high dimension. In much organization where large number of confidential data is available, such data must be secured. The personal data may be misused, for a variety of purposes. In order to alleviate these concerns, a number of techniques have recently been proposed in order to perform the data mining tasks in a privacy-preserving way. There are several anonymization techniques available such as generalization and bucketization that are designed for privacy preservation of microdata publishing. But it has been seen that for high dimension data generalization looses the information, bucketization on other hand does not prevent membership disclosure. We present another anonymization technique known as Slicing. The significance of using slicing is that it can handle high dimension data. Slicing preserves better data utility than generalization and also prevents membership disclosure. This paper focus on effective method that can be used for providing better data utility and can handle high-dimensional data.
Geometric Data Perturbation For Privacy Preserving Outsourced Data Mining
 
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ChennaiSunday Systems Pvt.Ltd We are ready to provide guidance to successfully complete your projects and also download the abstract, base paper from our website IEEE 2012 Java: http://www.chennaisunday.com/ieee-2012-java-projects.html IEEE 2012 Dot Net: http://www.chennaisunday.com/ieee-2012-projects.html IEEE 2011 JAVA: http://www.chennaisunday.com/ieee-2011-java-projects.html IEEE 2011 DOT NET: http://www.chennaisunday.com/ieee-2011-projects.html IEEE 2010 JAVA: http://www.chennaisunday.com/ieee-2010-java-projects.html IEEE 2010 DOT NET: http://www.chennaisunday.com/ieee-2010-dotnet-projects.html Real Time APPLICATION: http://www.chennaisunday.com/softwareprojects.html Contact: 9566137117/ 044-42046569 -- *Contact * * P.Sivakumar MCA Director ChennaiSunday Systems Pvt Ltd Phone No: 09566137117 New No.82, 3rd Floor, Arcot Road, Kodambakkam, Chennai - 600 024. URL: www.chennaisunday.com Location: http://www.chennaisunday.com/mapview.html
Views: 484 siva6351
Enabling Multilevel Trust In Privacy Preserving Data Mining
 
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ChennaiSunday Systems Pvt.Ltd We are ready to provide guidance to successfully complete your projects and also download the abstract, base paper from our website IEEE 2012 Java: http://www.chennaisunday.com/ieee-2012-java-projects.html IEEE 2012 Dot Net: http://www.chennaisunday.com/ieee-2012-projects.html IEEE 2011 JAVA: http://www.chennaisunday.com/ieee-2011-java-projects.html IEEE 2011 DOT NET: http://www.chennaisunday.com/ieee-2011-projects.html IEEE 2010 JAVA: http://www.chennaisunday.com/ieee-2010-java-projects.html IEEE 2010 DOT NET: http://www.chennaisunday.com/ieee-2010-dotnet-projects.html Real Time APPLICATION: http://www.chennaisunday.com/softwareprojects.html Contact: 9566137117/ 044-42046569 -- *Contact * * P.Sivakumar MCA Director ChennaiSunday Systems Pvt Ltd Phone No: 09566137117 New No.82, 3rd Floor, Arcot Road, Kodambakkam, Chennai - 600 024. URL: www.chennaisunday.com Location: http://www.chennaisunday.com/mapview.html
Views: 919 siva6351
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: 328 jpinfotechprojects
Privacy Preserving Data Sharing With Anonymous ID Assignment 2013 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 Privacy Preserving Data Sharing With Anonymous ID Assignment 2013 IEEE An algorithm for anonymous sharing of private data among parties is developed. This technique is used iteratively to assign these nodes ID numbers ranging from 1 to N. This assignment is anonymous in that the identities received are unknown to the other members of the group. Resistance to collusion among other members is verified in an information theoretic sense when private communication channels are used. This assignment of serial numbers allows more complex data to be shared and has applications to other problems in privacy preserving data mining, collision avoidance in communications and distributed database access. The required computations are distributed without using a trusted central authority. Existing and new algorithms for assigning anonymous IDs are examined with respect to trade-offs between communication and computational requirements. The new algorithms are built on top of a secure sum data mining operation using Newton's identities and Sturm's theorem. An algorithm for distributed solution of certain polynomials over finite fields enhances the scalability of the algorithms. Markov chain representations are used to find statistics on the number of iterations required, and computer algebra gives closed form results for the completion rates.
Views: 1058 jpinfotechprojects
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: 263269 Last moment tuitions
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: 99 jpinfotechprojects
Privacy Preserving Data Mining Hadoop Projects | Privacy Preserving Data Mining Hadoop Thesis
 
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Contact Best Phd Projects Visit us: http://www.phdprojects.org/
Views: 47 PHD PROJECTS
PPHOPCM: Privacy-preserving High-order Possibilistic c-Means Algorithm for Big Data Clustering
 
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PPHOPCM: Privacy-preserving High-order Possibilistic c-Means Algorithm for Big Data Clustering with 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 As one important technique of fuzzy clustering in data mining and pattern recognition, the possibilistic c-means algorithm (PCM) has been widely used in image analysis and knowledge discovery. However, it is difficult for PCM to produce a good result for clustering big data, especially for heterogenous data, since it is initially designed for only small structured dataset. To tackle this problem, the paper proposes a high-order PCM algorithm (HOPCM) for big data clustering by optimizing the objective function in the tensor space. Further, we design a distributed HOPCM method based on MapReduce for very large amounts of heterogeneous data. Finally, we devise a privacy-preserving HOPCM algorithm (PPHOPCM) to protect the private data on cloud by applying the BGV encryption scheme to HOPCM, In PPHOPCM, the functions for updating the membership matrix and clustering centers are approximated as polynomial functions to support the secure computing of the BGV scheme. Experimental results indicate that PPHOPCM can effectively cluster a large number of heterogeneous data using cloud computing without disclosure of private data.
Views: 242 JPINFOTECH PROJECTS
Data Mining and Privacy
 
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Made with http://biteable.com
Views: 175 Jason Alaee
Slicing A New Approach to Privacy Preserving Data Publishing
 
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Slicing: A New Approach for Privacy Preserving Data Publishing Abstract: Several anonymization techniques, such as generalization and bucketization, have been designed for privacy preserving microdata publishing. Recent work has shown that generalization loses considerable amount of information, especially for high-dimensional data. Bucketization, on the other hand, does not prevent membership disclosure and does not apply for data that do not have a clear separation between quasi-identifying attributes and sensitive attributes. In this paper, we present a novel technique called slicing, which partitions the data both horizontally and vertically. We show that slicing preserves better data utility than generalization and can be used for membership disclosure protection. Another important advantage of slicing is that it can handle high-dimensional data. We show how slicing can be used for attribute disclosure protection and develop an efficient algorithm for computing the sliced data that obey the ℓ-diversity requirement. Our workload experiments confirm that slicing preserves better utility than generalization and is more effective than bucketization in workloads involving the sensitive attribute. Our experiments also demonstrate that slicing can be used to prevent membership disclosure.
Views: 37 1 Crore Projects
Privacy-preserving High-order Possibilistic c-Means Algorithm for Big Data Clustering with CC
 
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Including Packages ======================= * Base Paper * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ https://myprojectbazaar.com Get Discount @ https://goo.gl/dhBA4M Chat Now @ http://goo.gl/snglrO Visit Our Channel: https://www.youtube.com/user/myprojectbazaar Mail Us: [email protected]
Views: 129 myproject bazaar
PPHOPCM: Privacy-preserving High-order Possibilistic c-Means Algorithm for Big Data Clustering
 
06:50
PPHOPCM: Privacy-preserving High-order Possibilistic c-Means Algorithm for Big Data Clustering with 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 As one important technique of fuzzy clustering in data mining and pattern recognition, the possibilistic c-means algorithm (PCM) has been widely used in image analysis and knowledge discovery. However, it is difficult for PCM to produce a good result for clustering big data, especially for heterogenous data, since it is initially designed for only small structured dataset. To tackle this problem, the paper proposes a high-order PCM algorithm (HOPCM) for big data clustering by optimizing the objective function in the tensor space. Further, we design a distributed HOPCM method based on MapReduce for very large amounts of heterogeneous data. Finally, we devise a privacy-preserving HOPCM algorithm (PPHOPCM) to protect the private data on cloud by applying the BGV encryption scheme to HOPCM, In PPHOPCM, the functions for updating the membership matrix and clustering centers are approximated as polynomial functions to support the secure computing of the BGV scheme. Experimental results indicate that PPHOPCM can effectively cluster a large number of heterogeneous data using cloud computing without disclosure of private data.
Views: 108 jpinfotechprojects
Random Decision Tree Privacy-preserving Data Mining Java Project
 
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Project Link : http://kasanpro.com/p/java/random-decision-tree-privacy-preserving-data-mining , Title :A Random Decision Tree Framework for Privacy-preserving Data Mining
Views: 521 kasanpro
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 in java 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: 470 jpinfotechprojects
slicing A New Approach for Privacy Preserving Data Publilshing
 
02:35
Several anonymization techniques, such as generalization and bucketization, have been designed for privacy preserving microdata publishing. Recent work has shown that generalization loses considerable amount of information, especially for high-dimensional data. Bucketization, on the other hand, does not prevent membership disclosure and does not apply for data that do not have a clear separation between quasi-identifying attributes and sensitive attributes. In this paper, we present a novel technique called slicing, which partitions the data both horizontally and vertically. We show that slicing preserves better data utility than generalization and can be used for membership disclosure protection. Another important advantage of slicing is that it can handle high-dimensional data. We show how slicing can be used for attribute disclosure protection and develop an efficient algorithm for computing the sliced data that obey the ℓ-diversity requirement. Our workload experiments confirm that slicing preserves better utility than generalization and is more effective than bucketization in workloads involving the sensitive attribute. Our experiments also demonstrate that slicing can be used to prevent membership disclosure.
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: 96 COLT
Final Year Projects | Privacy-Preserving Mining of Association Rules From Outsourced Transaction
 
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Including Packages ======================= * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-778-1155 +91 958-553-3547 +91 967-774-8277 Visit Our Channel: http://www.youtube.com/clickmyproject Mail Us: [email protected] chat: http://support.elysiumtechnologies.com/support/livechat/chat.php
Views: 846 myproject bazaar
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.
Slicing: A New Approach for Privacy Preserving Data Publishing 2012 IEEE JAVA
 
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Slicing: A New Approach for Privacy Preserving Data Publishing 2012 IEEE JAVA TO GET THIS PROJECT IN ONLINE OR THROUGH TRAINING SESSIONS CONTACT: Chennai Office: JP INFOTECH, Old No.31, New No.86, 1st Floor, 1st Avenue, Ashok Pillar, Chennai – 83. Landmark: Next to Kotak Mahendra Bank / Bharath Scans. Landline: (044) - 43012642 / Mobile: (0)9952649690 Pondicherry Office: JP INFOTECH, #45, Kamaraj Salai, Thattanchavady, Puducherry – 9. Landmark: Opp. To Thattanchavady Industrial Estate & Next to VVP Nagar Arch. Landline: (0413) - 4300535 / Mobile: (0)8608600246 / (0)9952649690 Email: [email protected], Website: http://www.jpinfotech.org, Blog: http://www.jpinfotech.blogspot.com Several anonymization techniques, such as generalization and bucketization, have been designed for privacy preserving microdata publishing. Recent work has shown that generalization loses considerable amount of information, especially for high dimensional data. Bucketization, on the other hand, does not prevent membership disclosure and does not apply for data that do not have a clear separation between quasi-identifying attributes and sensitive attributes. In this paper, we present a novel technique called slicing, which partitions the data both horizontally and vertically. We show that slicing preserves better data utility than generalization and can be used for membership disclosure protection. Another important advantage of slicing is that it can handle high-dimensional data. We show how slicing can be used for attribute disclosure protection and develop an efficient algorithm for computing the sliced data that obey the '-diversity requirement. Our workload experiments confirm that slicing preserves better utility than generalization and is more effective than bucketization in workloads involving the sensitive attribute. Our experiments also demonstrate that slicing can be used to prevent membership disclosure.
Views: 2583 jpinfotechprojects
Privacy-Preserving Outsourced Association Rule Mining on Vertically Partitioned Databases
 
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Privacy-Preserving Outsourced Association Rule Mining on Vertically Partitioned Databases 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 Association rule mining and frequent itemset mining are two popular and widely studied data analysis techniques for a range of applications. In this paper, we focus on privacy preserving mining on vertically partitioned databases. In such a scenario, data owners wish to learn the association rules or frequent itemsets from a collective dataset, and disclose as little information about their (sensitive) raw data as possible to other data owners and third parties. To ensure data privacy, we design an efficient homomorphic encryption scheme and a secure comparison scheme. We then propose a cloud-aided frequent itemset mining solution, which is used to build an association rule mining solution. Our solutions are designed for outsourced databases that allow multiple data owners to efficiently share their data securely without compromising on data privacy. Our solutions leak less information about the raw data than most existing solutions. In comparison to the only known solution achieving a similar privacy level as our proposed solutions, the performance of our proposed solutions is 3 to 5 orders of magnitude higher. Based on our experiment findings using different parameters and datasets, we demonstrate that the run time in each of our solutions is only one order higher than that in the best non-privacy-preserving data mining algorithms. Since both data and computing work are outsourced to the cloud servers, the resource consumption at the data owner end is very low.
Views: 366 jpinfotechprojects
Slicing: A New Approach to Privacy Preserving Data Publishing
 
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Title: Slicing: A New Approach to Privacy Preserving Data Publishing Is Developed in J2EE(Struts with Hibernate) by Mirror Technologies Pvt Ltd -- Vadapalani, Chennai. Domain: Data Mining Key Features: 1) Several anonymization techniques, such as generalization and bucketization, have been designed for privacy preserving microdata publishing. 2) Our workload experiments confirm that slicing preserves better utility than generalization and is more effective than bucketization in workloads involving the sensitive attribute. Visit http://www.lbenchindia.com/ For more details contact: Mirror Technologies Pvt Ltd #73 & 79, South Sivan kovil Street, Vadapalani, Chennai, Tamil Nadu. Telephone: +91-44-42048874. Phone: 9381948474, 9381958575. E-Mail: [email protected], [email protected]
Views: 481 Learnbench India
Managing Privacy Of Sensitive Attributes Using MFSARNN Clustering With Optimization Technique
 
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Title: Managing Privacy Of Sensitive Attributes Using MFSARNN Clustering With Optimization Technique Domain: Data Mining Key Features: 1. The slicing algorithm partitions the data into vertical and horizontal columns. The attributes and tuples in the slicing algorithm is clustered based on their similarity. In vertical partitioning, the attributes are grouped by Modified Fully Self Adaptive Resonance Neural Networks (MFSARNN). 2. The cluster formation has been improved by Genetic Algorithm based feature selection. In the horizontal partition, the tuples are grouped by Meta heuristic Fireflies Algorithm with Minkowsi Distance Measure (MFAMD). In this way the proposed system overcomes the privacy threats such as Identity disclosure, Attribute disclosure and Membership disclosure. 3. Slicing is one of the Anonymization techniques which is used to improve the privacy in data publishing. The slicing algorithm has been working with three different phases like, attribute partitioning, column generalization and tuple partitioning. The slicing algorithm partition the data set into vertical partition and horizontal partition. 4. Cluster analysis is the process of grouping the related information together, which is used for further processing and classification. Clustering techniques work with the high dimensionality of the data and produces the best result without losing the data and it also utilizes the data for entire processing. For this reason, slicing algorithm is working with clustering techniques. Clustering of attributes in the vertical partition is done by using Modified Fully Self Adaptive Resonance Neural Network (MFSARNN). 5. The working of MFSARNN as follows. Initially the sensitive features are selected by using a feature selection method. So, in the proposed system Genetic Algorithm is used for feature selection with three different steps like selection, crossover and mutation. In GA fitness function is one of the most important parameter which is used to select the sensitive features from the data. For more details contact: E-Mail: [email protected] Buy Whole Project Kit for Rs 5000%. Project Kit: • 1 Review PPT • 2nd Review PPT • Full Coding with described algorithm • Video File • Full Document Note: *For bull purchase of projects and for outsourcing in various domains such as Java, .Net, .PHP, NS2, Matlab, Android, Embedded, Bio-Medical, Electrical, Robotic etc. contact us. *Contact for Real Time Projects, Web Development and Web Hosting services. *Comment and share on this video and win exciting developed projects for free of cost. Search Terms: 1. 2017 ieee projects 2. latest ieee projects in java 3. latest ieee projects in data mining 4. 2016 – 2017 data mining projects 5. 2016 – 2017 best project center in Chennai 6. best guided ieee project center in Chennai 7. 2016 – 2017 ieee titles 8. 2016 – 2017 base paper 9. 2016 – 2017 java projects in Chennai, Coimbatore, Bangalore, and Mysore 10. time table generation projects 11. instruction detection projects in data mining, network security 12. 2016 – 2017 data mining weka projects 13. 2016 – 2017 b.e projects 14. 2016 – 2017 m.e projects 15. 2016 – 2017 final year projects 16. affordable final year projects 17. latest final year projects 18. best project center in Chennai, Coimbatore, Bangalore, and Mysore 19. 2017 Best ieee project titles 20. best projects in java domain 21. free ieee project in Chennai, Coimbatore, Bangalore, and Mysore 22. 2016 – 2017 ieee base paper free download 23. 2016 – 2017 ieee titles free download 24. best ieee projects in affordable cost 25. ieee projects free download 26. 2017 data mining projects 27. 2017 ieee projects on data mining 28. 2017 final year data mining projects 29. 2017 data mining projects for b.e 30. 2017 data mining projects for m.e 31. 2017 latest data mining projects 32. latest data mining projects 33. latest data mining projects in java 34. data mining projects in weka tool 35. data mining in intrusion detection system 36. intrusion detection system using data mining 37. intrusion detection system using data mining ppt 38. intrusion detection system using data mining technique 39. data mining approaches for intrusion detection 40. data mining in ranking system using weka tool 41. data mining projects using weka 42. data mining in bioinformatics using weka 43. data mining using weka tool 44. data mining tool weka tutorial 45. data mining abstract 46. data mining base paper 47. data mining research papers 2016 - 2017 48. 2016 - 2017 data mining research papers 49. 2017 data mining research papers 50. data mining IEEE Projects
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.
An Efficient and Fine-grained Big Data Access Control Scheme with Privacy-preserving Policy
 
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An Efficient and Fine-grained Big Data Access Control Scheme with Privacy-preserving Policy 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 How to control the access of the huge amount of big data becomes a very challenging issue, especially when big data are stored in the cloud. Ciphertext-Policy Attribute based Encryption (CP-ABE) is a promising encryption technique that enables end-users to encrypt their data under the access policies defined over some attributes of data consumers and only allows data consumers whose attributes satisfy the access policies to decrypt the data. In CP-ABE, the access policy is attached to the ciphertext in plaintext form, which may also leak some private information about end-users. Existing methods only partially hide the attribute values in the access policies, while the attribute names are still unprotected. In this paper, we propose an efficient and fine-grained big data access control scheme with privacy-preserving policy. Specifically, we hide the whole attribute (rather than only its values) in the access policies. To assist data decryption, we also design a novel Attribute Bloom Filter to evaluate whether an attribute is in the access policy and locate the exact position in the access policy if it is in the access policy. Security analysis and performance evaluation show that our scheme can preserve the privacy from any LSSS access policy without employing much overhead.
Views: 785 jpinfotechprojects
Privacy Preserving Outsourced Association Rule Mining on Vertically Partitioned Databases
 
00:15
Privacy Preserving Outsourced Association Rule Mining on Vertically Partitioned Databases 2016-2017 MICANS INFOTECH offers Projects in CSE ,IT, EEE, ECE, MECH , MCA. MPHIL , BSC, in various domains JAVA ,PHP, DOT NET , ANDROID , MATLAB , NS2 , EMBEDDED , VLSI , APPLICATION PROJECTS , IEEE PROJECTS. CALL : +91 90036 28940 +91 94435 11725 [email protected] WWW.MICANSINFOTECH.COM COMPANY PROJECTS, INTERNSHIP TRAINING, MECHANICAL PROJECTS, ANSYS PROJECTS, CAD PROJECTS, CAE PROJECTS, DESIGN PROJECTS, CIVIL PROJECTS, IEEE MCA PROJECTS, IEEE M.TECH PROJECTS, IEEE PROJECTS, IEEE PROJECTS IN PONDY, IEEE PROJECTS, EMBEDDED PROJECTS, ECE PROJECTS PONDICHERRY, DIPLOMA PROJECTS, FABRICATION PROJECTS, IEEE PROJECTS CSE, IEEE PROJECTS CHENNAI, IEEE PROJECTS CUDDALORE, IEEE PROJECTS IN PONDICHERRY, PROJECT DEVELOPMENT CENTRE
Slicing A New Approach to Privacy Preserving Data Publishing(Java)
 
06:10
Several anonymization techniques, such as generalization and bucketization, have been designed for privacy preserving microdata publishing. Recent work has shown that generalization loses considerable amount of information, especially for high-dimensional data. Bucketization, on the other hand, does not prevent membership disclosure and does not apply for data that do not have a clear separation between quasi-identifying attributes and sensitive attributes. In this paper, we present a novel technique called slicing, which partitions the data both horizontally and vertically. We show that slicing preserves better data utility than generalization and can be used for membership disclosure protection. Another important advantage of slicing is that it can handle high-dimensional data. We show how slicing can be used for attribute disclosure protection and develop an efficient algorithm for computing the sliced data that obey the ℓ-diversity requirement. Our workload experiments confirm that slicing preserves better utility than generalization and is more effective than bucketization in workloads involving the sensitive attribute. Our experiments also demonstrate that slicing can be used to prevent membership disclosure.
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
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
A RANDOM DECISION TREE FRAMEWORK FOR PRIVACY PRESERVING DATA MINING
 
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Gagner Technologies offers M.E projects based on IEEE 2014 . M.Phil Research projects,Final Year Projects, M.E projects 2014-2015, mini projects 2014-2015, Real Time Projects, Final Year Projects for BE ECE, CSE, IT, MCA, B TECH, ME, M SC (IT), BCA, BSC CSE, IT IEEE 2013 Projects in Data Mining, Distributed System, Mobile Computing, Networks, Networking. IEEE2014-2015 projects. Final Year Projects at Chennai, IEEE Software Projects, Engineering Projects, MCA projects, BE projects, JAVA projects, J2EE projects, .NET projects, Students projects, Final Year Student Projects, IEEE Projects 2014-2015, Real Time Projects, Final Year Projects for BE ECE, CSE, IT, MCA, B TECH, ME, M SC (IT), BCA, BSC CSE, IT,software Engineering,NS2 projects,Mechanical Projects,VLSI projects,Matlab Projects For more details contact below Address No 1,South Dhandapani street(opposite to T.Nagar Bus Stand),T.Nagar,chennai-17 call:8680939422,9962221452 Mail to:[email protected]