Target-based privacy preserving association rule mining

Authors:
Madhu Ahluwalia;Aryya Gangopadhyay;Zhiyuan Chen;Yelena Yesha
Affiliations:
University of Maryland Baltimore County (UMBC), Baltimore, MD;University of Maryland Baltimore County (UMBC), Baltimore, MD;University of Maryland Baltimore County (UMBC), Baltimore, MD;University of Maryland Baltimore County (UMBC), Baltimore, MD
Venue:
Proceedings of the 2011 ACM Symposium on Applied Computing
Year:
2011

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Abstract

Association rule mining is an important data mining task applicable across many commercial and scientific domains. There are instances when association analysis must be conducted by a third party over data located at a central point, but updated from several source locations. The source locations may not allow tracking changes. The target location must then take charge of the changed data detection and privatization process. We propose a solution to conduct privacy preserving association rule mining on such data. An evaluation of our approach shows that compared to existing approaches, it renders higher privacy, preserves 90% -100% of the rules and is efficient for 10% database changes.