Privacy protection of binary confidential data against deterministic, stochastic, and insider threat

Robert Garfinkel, Ram Gopal, Paulo Goes

Research output: Contribution to journalArticlepeer-review

43 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)749-764
Number of pages16
JournalManagement Science
Issue number6
StatePublished - Jun 2002


  • Categorical Data
  • Confidentiality Protection
  • Database Security
  • Inference Disclosure

ASJC Scopus subject areas

  • Strategy and Management
  • Management Science and Operations Research


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