A new scheme on privacy preserving association rule mining

Authors:
Nan Zhang;Shengquan Wang;Wei Zhao
Affiliations:
Texas A&M University, College Station, TX;Texas A&M University, College Station, TX;Texas A&M University, College Station, TX
Venue:
PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
Year:
2004

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Abstract

We address the privacy preserving association rule mining problem in a system with one data miner and multiple data providers, each holds one transaction. The literature has tacitly assumed that randomization is the only effective approach to preserve privacy in such circumstances. We challenge this assumption by introducing an algebraic techniques based scheme. Compared to previous approaches, our new scheme can identify association rules more accurately but disclose less private information. Furthermore, our new scheme can be readily integrated as a middleware with existing systems.