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 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 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:-
ü Processor - Pentium –IV
ü Speed - 1.1
Ghz
ü RAM - 256
MB(min)
ü Hard Disk -
20 GB
ü Key Board -
Standard Windows Keyboard
ü Mouse - Two
or Three Button Mouse
ü Monitor - SVGA
SOFTWARE CONFIGURATION:-
ü Operating System : Windows XP
ü Programming Language :
JAVA/J2EE.
ü Java Version :
JDK 1.6 & above.
ü 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.
