Secure set intersection cardinality with application to association rule mining

Article type: Research Article

Authors: Vaidya, Jaideep^{; *} | Clifton, Chris

Affiliations: Department of Computer Sciences, Purdue University, 250 N University St. West Lafayette, IN 47907-2066, USA. Tel.: +1 765 494 6408; Fax: +1 765 494 0739; E-mail: jsvaidya@cs.purdue.edu. Tel.: +1 765 494 6005; Fax: +1 765 494 0739; E-mail: clifton@cs.purdue.edu

Correspondence: [*] Corresponding author.

Abstract: There has been concern over the apparent conflict between privacy and data mining. There is no inherent conflict, as most types of data mining produce summary results that do not reveal information about individuals. The process of data mining may use private data, leading to the potential for privacy breaches. Secure Multiparty Computation shows that results can be produced without revealing the data used to generate them. The problem is that general techniques for secure multiparty computation do not scale to data-mining size computations. This paper presents an efficient protocol for securely determining the size of set intersection, and shows how this can be used to generate association rules where multiple parties have different (and private) information about the same set of individuals.

DOI: 10.3233/JCS-2005-13401

Journal: Journal of Computer Security, vol. 13, no. 4, pp. 593-622, 2005