Cryptology ePrint Archive: Report 2015/671

Privacy-preserving Frequent Itemset Mining for Sparse and Dense Data

Peeter Laud and Alisa Pankova

Abstract: Frequent itemset mining is a task that can in turn be used for other purposes such as associative rule mining. One problem is that the data may be sensitive, and its owner may refuse to give it for analysis in plaintext. There exist many privacy-preserving solutions for frequent itemset mining, but in any case enhancing the privacy inevitably spoils the efficiency. Leaking some less sensitive information such as data density might improve the efficiency. In this paper, we devise an approach that works better for sparse matrices and compare it to the related work that uses similar security requirements on similar secure multiparty computation platform.

Category / Keywords: applications / frequent itemset mining, secure multiparty computation

Date: received 3 Jul 2015

Contact author: peeter at cyber ee

Available format(s): PDF | BibTeX Citation

Version: 20150705:180312 (All versions of this report)

Short URL: ia.cr/2015/671

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