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Disclosure Control of Confidential Data by Applying Pac Learning Theory

Disclosure Control of Confidential Data by Applying Pac Learning Theory

Ling He, Haldun Aytug, Gary J. Koehler
Copyright: © 2010 |Volume: 21 |Issue: 4 |Pages: 13
ISSN: 1063-8016|EISSN: 1533-8010|EISBN13: 9781613502242|DOI: 10.4018/jdm.2010100106
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MLA

He, Ling, et al. "Disclosure Control of Confidential Data by Applying Pac Learning Theory." JDM vol.21, no.4 2010: pp.111-123. http://doi.org/10.4018/jdm.2010100106

APA

He, L., Aytug, H., & Koehler, G. J. (2010). Disclosure Control of Confidential Data by Applying Pac Learning Theory. Journal of Database Management (JDM), 21(4), 111-123. http://doi.org/10.4018/jdm.2010100106

Chicago

He, Ling, Haldun Aytug, and Gary J. Koehler. "Disclosure Control of Confidential Data by Applying Pac Learning Theory," Journal of Database Management (JDM) 21, no.4: 111-123. http://doi.org/10.4018/jdm.2010100106

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Abstract

This paper examines privacy protection in a statistical database from the perspective of an intruder using learning theory to discover private information. With the rapid development of information technology, massive data collection is relatively easier and cheaper than ever before. The challenge is how to provide database users with reliable and useful data while protecting the privacy of the confidential information. This paper discusses how to prevent disclosing the identity of unique records in a statistical database. The authors’ research extends previous work and shows how much protection is necessary to prevent an adversary from discovering confidential data with high probability at small error.

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