Incentive compatible privacy preserving data analysis(9611502400)

100 %
0 %
Information about Incentive compatible privacy preserving data analysis(9611502400)
Education

Published on March 4, 2014

Author: NaveenKumar358

Source: slideshare.net

Description

Dear IT Aspirants ,
Greetings from Embedpark , Bangalore!!!
Embedpark Innovations pvt ltd, Bangalore offers Total Developement solutions and Embedpark Innovations projects for freshers, working professionals and corporate in the below mentioned technologies:-
Final Year Projects Include:
1. Embedded Systems(Embedded / ARM / PIC/ MC/ MP)
2. Robotics
3. Linux Based Projects
4. Developement on FPGA / VLSI
5. Android
6. Mobile Applications
7. Website Developement
8. Database Design and Applicatios
9. MS ASP.Net, C# .Net, VB.Net, VC++ .net
10.JAVA / Advance - Java Projct
11.Big Data Hadoop
12.PHP
Embedpark Innovations pvt ltd, Bangalore has a dedicated total Developement & solutions in the Industry.
For further IEEE Projects details about Embedded System , VLSI, Android, Dot net, JAVA for IEEE 2013-14 Projects,
Source code+Documents+Review Document Slides+Softwares+Explanation+Execution
Feel free to contact the concerned person mentioned below:
Contact:Name: Naveen Kumar
Address: Embedpark Innovations Pvt.Ltd, #20/233, Manandi Towers, 9th Main Road, Above ING Vysya Bank, 2nd floor , Jayanagar 3rd block Bangalore, India-560011
Contact: 9886733705 / 9886289694

Incentive Compatible Privacy-Preserving Data Analysis Abstract: The competing parties who have private data may collaboratively conduct privacy preserving distributed data analysis (PPDA) tasks to learn beneficial data models or analysis results. For example , different credit card companies may try to build better models for credit card fraud detection through PPDA tasks. Similarly, competing companies in the same industry may try to combine their sales data to build models that may predict the future sales. In many of these cases, 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 or not participating parties are truthful about their private input data. In other words, unless proper incentives are set, even current PPDA techniques cannot prevent participating parties from modifying their private inputs. This raises the question of how to design incentive compatible privacypreserving data analysis techniques that motivate participating parties to provide truthful input data. In this paper, we first develop key theorems, then base on these theorem, we analyze what types of privacy-preserving data analysis tasks could be conducted in a way that telling the truth is the best choice for any participating party.

Architecture: Existing system: Even though privacy-preserving data analysis techniques guarantee that nothing other than the final result is disclosed, whether or not participating parties provide truthful input data cannot be verified. Although certain PPDA techniques guarantee that nothing other than the final analysis result is revealed, it is impossible to verify whether or not participating parties are truthful about their private input data. In other words, unless proper incentives are set, even current PPDA techniques cannot prevent participating parties from modifying their private inputs.

Proposed System: In design incentive compatible privacy-preserving data analysis techniques that motivate participating parties to provide truthful input data. In this paper, we first develop key theorems, then base on these theorem, we analyze what types of privacy-preserving data analysis tasks could be conducted in a way that telling the truth is the best choice for any participating party. Secure multi-party computation (SMC) has recently emerged as an answer to this problem. Implementation Modules: Main Modules: 1. 2. 3. 4. Privacy-Preserving Data Analysis Non-Cooperative Computation ANALYZING DATA ANALYSIS TASKS IN THE NCC MODEL Privacy Preserving Association Rule Mining Privacy-Preserving Data Analysis: The privacy preserving data analysis protocols assume that participating parties are truthful about their private input data . The techniques developed in assume that each party has an internal device that can verify whether they are telling the truth or not. In our work, we do not assume the existence of such a device. Instead, we try to make sure that providing the true input is the best choice for a participating party. Non-Cooperative Computation: In the NCC model, each party participates in a protocol to learn the output of some given function f over the joint inputs of the parties. First, all participating parties send their private inputs securely to a trusted third party (TTP), then TTP computes f and sends back the result to every participating party. The NCC model makes the following assumptions.

1) Correctness: The first priority for every participating party is to learn the correct result; 2) Exclusiveness: If possible, every participating party prefers to learn the correct result exclusively. Analyzing Data Analysis Tasks Ii The NCC Model: Combining the two concepts DNCC and SMC, we can analyze privacy preserving data analysis tasks (without utilizing a TTP)that are incentive compatible. We next prove several such important tasks (as function with boolean output, set operations, linear functions, etc) that either satisfy or do not satisfy the DNCC model. Also, note that the data analysis tasks analyzed next have practical SMC implementations. Privacy Preserving Association Rule Mining: (or)Security System: The association rulemining and analyze whether the association rule mining can be done in an incentive compatible manner over horizontally and vertically partitioned databases.The Security Code is valid means retrieve the datas otherwise You are a fraud user. System Configuration: HARDWARE REQUIREMENTS:

Hardware - Pentium Speed - 1.1 GHz RAM - 1GB Hard Disk - 20 GB Floppy Drive - 1.44 MB Key Board - Standard Windows Keyboard Mouse - Two or Three Button Mouse Monitor - SVGA SOFTWARE REQUIREMENTS: Operating System : Windows Technology : Java and J2EE Web Technologies : Html, JavaScript, CSS IDE : My Eclipse Web Server : Tomcat Tool kit : Android Phone Database : My SQL Java Version : J2SDK1.5

Add a comment

Related presentations

Related pages

Incentive Compatible Privacy-Preserving Data Analysis

sign incentive compatible privacy-preserving data analysis ... incentive compatible under the assumption that partici-pating parties prefer to learn the ...
Read more

Incentive Compatible Privacy-Preserving Data Analysis

... the competing parties have different incentives. ... private inputs.incentive compatible privacy-preserving data analysis techniques This raises ...
Read more

Privacy-Preserving Data Analysis Techniques by using ...

Privacy-Preserving Data Analysis ... In design incentive compatible privacy-preserving data ... analysis tasks that are incentive compatible.
Read more

Incentive Compatible Privacy-Preserving Data Analysis

Incentive Compatible Privacy-Preserving Data ... This raises the question of how to design incentive compatible privacy-preserving data analysis ...
Read more

Incentive Compatible Privacy-Preserving Data Analysis

... conduct privacy-preserving distributed data analysis ... to design incentive compatible privacy-preserving data analysis techniques ...
Read more

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL ...

Incentive Compatible Privacy-Preserving Data Analysis ... This raises the question of how to design incentive compatible privacy-preserving data ...
Read more

System Configuration: - Chennai Sunday

... conduct privacy. preserving. distributed data analysis ... compatible privacy-preserving data analysis. ... in an incentive compatible ...
Read more

JAVA--2013 Incentive Compatible Privacy-Preserving Data ...

JAVA--2013 Incentive Compatible Privacy-Preserving Data Analysis LT LIOTechprojects. Subscribe Subscribed Unsubscribe 106 106. Loading... Loading...
Read more

Sharing Private Data for Building Data Analysis Models

privacy-preserving data analysis protocols have been designed using cryptographic techniques for security purpose. ... compatible incentive fashion.
Read more