Incentive compatible privacy preserving data analysis
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This document discusses incentive compatible privacy-preserving data analysis. It proposes techniques that would motivate participating parties in data analysis tasks to provide truthful private inputs. Key theorems are developed to analyze what types of distributed data analysis functionalities could be implemented in a way that parties are incentivized to provide their true private data, such as certain data analysis tasks being in the best interest of parties to tell the truth. The number of dishonest parties is assumed to be at most one less than the total number of parties engaged in the analysis.
Incentive compatible privacy preserving data analysis
- 1. Incentive Compatible Privacy-Preserving Data Analysis ABSTRACT: 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. EXISTING SYSTEM: PRIVACY and security, particularly maintaining confidentiality of data, have become a challenging issue with advances in information and communication
- 2. technology. The ability to communicate and share data has many benefits, and the idea of an omniscient data source carries great value to research and building accurate data analysis models. For example, for credit card companies to build more comprehensive and accurate fraud detection system, credit card transaction data from various companies may be needed to generate better data analysis models. DISADVANTAGES OF EXISTING SYSTEM: To our knowledge, all the existing techniques assume that each participating party use its true data during the distributed data mining protocol execution. PROPOSED SYSTEM: In this paper, we analyze what types of distributed functionalities could be implemented in an incentive compatible fashion. In other words, we explore which functionalities can be implemented in a way that participating parties have the incentive to provide their true private inputs upon engaging in the corresponding SMC protocols. In this paper, we assume that the number of malicious or dishonest participating parties can be at most n _ 1, where n is the number of parties. This assumption is very general since most existing works in the area of privacy-preserving data analysis assume either all participating parties are honest (or semi-honest) or the
- 3. majority of participating parties are honest. Thus, we extend the non-cooperative computation definitions to incorporate cases where there are multiple dishonest parties. In addition, we show that from incentive compatibility point of view, most data analysis tasks need to be analyzed only for two party cases. ADVANTAGES OF PROPOSED SYSTEM: Privacy preserving data analysis tasks SYSTEM CONFIGURATION:- HARDWARE CONFIGURATION:- Processor - Pentium –IV Speed - 1.1 Ghz RAM - 256 MB(min) Hard Disk - 20 GB Key Board - Standard Windows Keyboard Mouse - Two or Three Button Mouse Monitor - SVGA
- 4. SOFTWARE CONFIGURATION:- Operating System : Windows XP Programming Language : JAVA/J2EE. Java Version : JDK 1.6 & above. Database : MYSQL REFERENCE: Murat Kantarcioglu and Wei Jiang, “Incentive Compatible Privacy-Preserving Data Analysis”, IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. 25, NO. 6, JUNE 2013.