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CLOSENESS:A NEW PRIVACY MEASURE F FOR DATA PUBLISHING

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BATCH DETAILS Batch Members: P.SNEHA LAKSHMI- 07G21A0592 B.RAMA - 07G21A0569 Y.TEJASWINI - 07G21A05A6 M.TEJAMMA - 07G21A05A5 BATCH NO:18 GUIDE : J.SAI KISHOR,M.Tech.,

ABSTRACT:

ABSTRACT The k-anonymity privacy requirement for publishing microdata requires that each equivalence class 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 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”.

EXISTING SYSTEM AND DRAWBACKS:

EXISTING SYSTEM AND DRAWBACKS Before data publishing privacy called to set security code. Each and every person need to register and getting security code. This is the waste of time. Another one is the public semantic searching and getting result for public person. This public person is not considered anonymous. Clearly, the released data containing such information about individuals should not be considered anonymous. Sometimes getting information via searching in particular name wise.

PROPOSED SYSTEM AND ADVANTAGES:

PROPOSED SYSTEM AND ADVANTAGES Can’t visible full information for the public person. Incase public person search for a particular person information the result is each and every splitting data’s then blocking or set substring of asterisk (*) using l-diversion and closeness. Here public person or unauthorized person is considered anonymous. We can analyse how much percentage of possible privacy loss. Here is also available checking utility (EMD) analyse using Anonymization Algorithm. You can identify easy to see closeness ratio. L-diversion and closeness is very low the security mode very high. Incase l-diversion and closeness is very high the security mode is very low.

SYSTEM REQUIREMENTS:

SYSTEM REQUIREMENTS HARDWARE SPECIFICATION Processor : Any Processor above 500 MHz. Ram : 128Mb. Hard Disk : 10 GB. Input device : Standard Keyboard and Mouse. Output device : VGA and High Resolution Monitor. SOFTWARE SPECIFICATION Operating System : Windows Family. Pages developed using : Java Server Pages and HTML. Techniques : Apache Tomcat Web Server 5.0, JDK 1.5 or higher Web Browser : Microsoft Internet Explorer. Data Bases : My SQL 5.0 Client Side Scripting : Java Script

MODULES:

MODULES Publishing Privacy L-diversion and closeness Anonymization Algorithms Data Processing

DATAFLOW DIAGRAM:

DATAFLOW DIAGRAM

USECASE DIAGRAM:

USECASE DIAGRAM

CONCLUSION:

CONCLUSION While k-anonymity protects against identity disclosure, it does not provide sufficient protection against attribute disclosure. The notion of `-diversity attempts to solve this problem. We have shown that `-diversity has a number of limitations and especially presented two attacks on `-diversity. Motivated by these limitations, we have proposed a novel privacy notion called “closeness”. We propose two instantiations: a base model called t-closeness and a more flexible privacy model called (n, t)-closeness. We explain the rationale of the (n, t)-closeness model and show that it achieves a better balance between privacy and utility.

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