A privacy preserving mining algorithm on distributed dataset

Authors:
Shen Hui-zhang;Zhao Ji-di;Yang Zhong-zhi
Affiliations:
Aetna School of Management, Shanghai Jiao Tong University, Shanghai, P.R. China;Aetna School of Management, Shanghai Jiao Tong University, Shanghai, P.R. China;Aetna School of Management, Shanghai Jiao Tong University, Shanghai, P.R. China
Venue:
FSKD'06 Proceedings of the Third international conference on Fuzzy Systems and Knowledge Discovery
Year:
2006

Citing 8
Cited 0

Security-control methods for statistical databases: a comparative study

ACM Computing Surveys (CSUR)
Data mining (Invited talk. Abstract only): crossing the Chasm

KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Privacy-preserving data mining

SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
On the design and quantification of privacy preserving data mining algorithms

PODS '01 Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Efficient Mining of Association Rules in Distributed Databases

IEEE Transactions on Knowledge and Data Engineering
Selective partial access to a database

ACM '76 Proceedings of the 1976 annual conference
Privacy preserving mining of association rules

Information Systems - Knowledge discovery and data mining (KDD 2002)
Maintaining data privacy in association rule mining

VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases

Quantified Score

Hi-index	0.00

Visualization

Abstract

The issue of maintaining privacy in data mining has attracted considerable attention over the last few years. The difficulty lies in the fact that the two metrics for evaluating privacy preserving data mining methods: privacy and accuracy are typically contradictory in nature. This paper addresses privacy preserving mining of association rules on distributed dataset. We present an algorithm, based on a probabilistic approach of distorting transactions in the dataset, which can provide high privacy of individual information and at the same time acquire a high level of accuracy in the mining result. Finally, we present experiment results that validate the algorithm.