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Privacy Protection of Binary Confidential Data Against Deterministic, Stochastic, and Insider Threat

Listed author(s):
  • Robert Garfinkel


    (School of Business Administration, University of Connecticut, Storrs, Connecticut 06269)

  • Ram Gopal


    (School of Business Administration, University of Connecticut, Storrs, Connecticut 06269)

  • Paulo Goes


    (School of Business Administration, University of Connecticut, Storrs, Connecticut 06269)

Registered author(s):

    A practical model and an associated method are developed for providing consistent, deterministically correct responses to ad-hoc queries to a database containing a field of binary confidential data. COUNT queries, i.e., the number of selected subjects whose confidential datum is positive, are to be answered. Exact answers may allow users to determine an individual's confidential information. Instead, the proposed technique gives responses in the form of a number plus a guarantee so that the user can determine an interval that is sure to contain the exact answer. At the same time, the method is also able to provide both deterministic and stochastic protection of the confidential data to the subjects of the database. Insider threat is defined precisely and a simple option for defense against it is given. Computational results on a simulated database are very encouraging in that most queries are answered with tight intervals, and that the quality of the responses improves with the number of subjects identified by the query. Thus the results are very appropriate for the very large databases prevalent in business and governmental organizations. The technique is very efficient in terms of both time and storage requirements, and is readily scalable and implementable.

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    Article provided by INFORMS in its journal Management Science.

    Volume (Year): 48 (2002)
    Issue (Month): 6 (June)
    Pages: 749-764

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    Handle: RePEc:inm:ormnsc:v:48:y:2002:i:6:p:749-764
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    1. Sumit Dutta Chowdhury & George T. Duncan & Ramayya Krishnan & Stephen F. Roehrig & Sumitra Mukherjee, 1999. "Disclosure Detection in Multivariate Categorical Databases: Auditing Confidentiality Protection Through Two New Matrix Operators," Management Science, INFORMS, vol. 45(12), pages 1710-1723, December.
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