PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Private Itemset Support Counting
Sven Laur, Helger Lipmaa and Taneli Mielikäinen
In: ICICS 2005, 10-13 Dec 2005,, Beijing, China.

Abstract

Private itemset support counting (PISC) is a basic building block of various privacy-preserving data mining algorithms. Briefly, in PISC, Client wants to know the support of her itemset in Server’s database with the usual privacy guarantees. First, we show that if the number of attributes is small, then a communication-efficient PISC protocol can be constructed from a communication-efficient oblivious transfer protocol. The converse is also true: any communication-efficient PISC protocol gives rise to a communicationefficient oblivious transfer protocol. Second, for the general case, we propose a computationally efficient PISC protocol with linear communication in the size of the database. Third, we show how to further reduce the communication by using various tradeoffs and random sampling techniques. Keywords: privacy-preserving data mining, private frequent itemset mining, private itemset support counting, private subset inclusion test.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
Information Retrieval & Textual Information Access
ID Code:1474
Deposited By:Taneli Mielikäinen
Deposited On:28 November 2005