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Activity Number: 204
Type: Contributed
Date/Time: Monday, August 7, 2006 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistical Computing
Abstract - #307565
Title: Maximum Entropy Data Camouflaging
Author(s): Kurt Pflughoeft*+ and Ehsan S. Soofi and Refik Soyer
Companies: Market Probe and University of Wisconsin-Milwaukee and The George Washington University
Address: 2655 N. Mayfair Road, Milwaukee, WI, 53226,
Keywords: maximum entropy distribution ; moment ; percentile ; statistical disclosure limitation ; intrusion
Abstract:

In the problem of data security the main concern is to avoid misuse by the intruders who seek information on individuals while providing useful information for statistical analysis to legitimate users. We propose a maximum entropy (ME) approach for masking the data. In the proposed procedure the released data maintains all the essential features of the actual data required for statistical analysis, but all the individual entries are completely camouflaged. Our ME approach is complementary to paradigm proposed by Keller-McNulty, Nakhleh, and Singpurwalla (2005) where the information content of the data, not the actual data, is masked through increasing the entropy of data-generating distribution. The proposed ME procedure will be illustrated and compared with the existing methods.

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