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Privacy preserving data mining in distributed databases

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Privacy concerns have become an important issue in Data Mining. This seminar deals with the problem of association rule mining from distributed vertically partitioned data with the goal of preserving the confidentiality of each database. Each site holds some attributes of each transaction, and the sites wish to work together to find globally valid association rules without revealing individual transaction data. This problem occurs, for example, when the same users access several electronic shops purchasing different items in each, and the shops like to cooperate to obtain valid global rules without compromising their private databases.

In this talk, we first review the work on privacy based rules mining in both centralized and distributed databases, and in both vertically and horizontally pertitioned databases. We then present two algorithms for discovering frequent item sets and two algorithms for extracting the association rules. We analyze the security, privacy and complexity properties of the algorithms and compare them to the best known algorithms of Vaidya and Clifton.

This talk is part of the Computer Laboratory Security Seminar series.

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