Secure Computation for Privacy Preserving Data Mining

Secure Computation for Privacy Preserving Data Mining

Yehuda Lindell
Copyright: © 2009 |Pages: 6
DOI: 10.4018/978-1-60566-010-3.ch266
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Abstract

The increasing use of data mining tools in both the public and private sectors raises concerns regarding the potentially sensitive nature of much of the data being mined. The utility to be gained from widespread data mining seems to come into direct conflict with an individual’s need and right to privacy. Privacy preserving data mining solutions achieve the somewhat paradoxical property of enabling a data mining algorithm to use data without ever actually “seeing” it. Thus, the benefits of data mining can be enjoyed, without compromising the privacy of concerned individuals.
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Main Thrust

In this short chapter, we will provide a succinct overview of secure multiparty computation, and how it can be applied to the problem of privacy preserving data mining. Our main focus will be on how security is formally defined, why this definitional approach is adopted, and what issues should be considered when defining security for privacy preserving data mining problems. Due to space constraints, the treatment in this chapter is both brief and informal. For more details, we refer the reader to (Goldreich, 2003) for a survey on cryptography and cryptographic protocols.

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