IEEE Project Titles, CLOSENESS: A NEW PRIVACY MEASURE FOR DATA PUBLISHING

.Net Projects ( major )	Java Projects ( major )	VB Projects ( major )	Php Projects ( major )	Vc++ Projects ( major )	SQL projects ( major )	IEEE ECE Projects
C&Ds projects ( major )	C++ projects ( major )	Perl projects ( major )	Cold Fusion Projects	ASP projects ( major )	Delphi projects ( major )	IEEE CSE Projects

Custom Search

What is 1000Projects
'1000projects.com' is an educational content website dedicated to finding and realizing Final Year Projects, IEEE Projects, Engineering Projects, Science Fair Projects, Project Topics, Project Ideas, Major Projects, Mini Projects, Paper Presentations, Presentation Topics, IEEE Topics, .Net Projects, Java Projects, PHP Projects, VB Projects, SQL Projects, C & DS Projects, C++ Projects, Perl Projects, ASP Projects, Delphi Projects, HTML Projects, Cold Fusion Projects, Java Script Projects, Btech Projects, BE Projects, MCA Projects, Mtech Projects, MBA Projects, Project on Software, CBSE Projects, Testing Projects, Embedded Projects, Chemistry Projects, Electronics Projects, Electrical Projects, Science Projects, Mechanical Projects, Mba project Reports, Placement papers, Sample Resumes, Entrance Exams, Technical Faq's, Puzzles, etc

how it works?

Everything on this site is submitted by the students in this professional community. You Can submit your Projects, Project Topics & Ideas to info.1000projects{at}gmail.com after you submit your project/project Idea/Abstract/Seminar Topics, These are being verified and approved by our administrator. after approval of this project/project Idea/Abstract/Seminar Topics, It can be shown on 1000projects.com so that other users can read/discuss it.The entire content on this website is Only For Educational Purpose, Non Commercial use!

Please help us/Other Users by sending projects/project Ideas/Abstracts/Seminar Topics. Thanking You!!!!!

CLOSENESS: A NEW PRIVACY MEASURE FOR DATA PUBLISHING
KNOWLEDGE AND DATA ENGINEERING
J2EE
The k-anonymity privacy requirement for publishing microdata requires that each equivalence class (i.e., a set of records that are indistinguishable from each other with respect to certain “identifying” attributes) contains at least k records. Recently, several authors have recognized that k-anonymity cannot prevent attribute disclosure. The notion of `-diversity has been proposed to address this; `-diversity requires that each equivalence class has at least ` well-represented (in Section 2) values for each sensitive attribute. In this article, we show that `-diversity has a number of limitations. In particular, it is neither necessary nor sufficient to prevent attribute disclosure. Motivated by these limitations, we propose a new notion of privacy called “closeness”. We first present the base model t-closeness, which requires that the distribution of a sensitive attribute in any equivalence class is close to the distribution of the attribute in the overall table (i.e., the distance between the two distributions should be no more than a threshold t). We then propose a more flexible privacy model called (n, t)-closeness that offers higher utility. We describe our desiderata for designing a distance measure between two probability distributions and present two distance measures. We discuss the rationale for using closeness as a privacy measure and illustrate its advantages through examples and experiments.