Privacy-Preserving Frequent Pattern Mining across Private Databases

Authors:
Ada Wai-Chee Fu;Raymond Chi-Wing Wong;Ke Wang
Affiliations:
Chinese University of Hong Kong;Chinese University of Hong Kong;Simon Fraser University
Venue:
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Year:
2005

Citing 6
Cited 4

Principles of distributed database systems (2nd ed.)

Principles of distributed database systems (2nd ed.)
Using Semi-Joins to Solve Relational Queries

Journal of the ACM (JACM)
Privacy preserving association rule mining in vertically partitioned data

Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining Association Rules from Stars

ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Information sharing across private databases

Proceedings of the 2003 ACM SIGMOD international conference on Management of data
k-TTP: a new privacy model for large-scale distributed environments

Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining

An efficient protocol for private and accurate mining of support counts

Pattern Recognition Letters
A cost-efficient and versatile sanitizing algorithm by using a greedy approach

FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 2
Implementation, optimization and performance tests of privacy preserving mechanisms in homogeneous collaborative association rules mining

OTM'11 Proceedings of the 2011th Confederated international conference on On the move to meaningful internet systems - Volume Part I
Privacy preserving mining maximal frequent patterns in transactional databases

DASFAA'12 Proceedings of the 17th international conference on Database Systems for Advanced Applications - Volume Part I

Quantified Score

Hi-index	0.00

Visualization

Abstract

Privacy consideration has much significance in the application of data mining. It is very important that the privacy of individual parties will not be exposed when data mining techniques are applied to a large collection of data about the parties. In many scenarios such as data warehousing or data integration, data from the different parties form a many-to-many schema. This paper addresses the problem of privacy-preserving frequent pattern mining in such a schema across two dimension sites. We assume that sites are not trusted and they are semi-honest. Our method is based on the concept of semi-join and does not involve data encryption which is used in most previous work. Experiments are conducted to study the efficiency of the proposed models.