A fast procedure for finding a tracker in a statistical database

Author:

Denning Dorothy E.1,Schlörer Jan2

Affiliation:

1. Purdue Univ., West Lafayette, IN

2. Univ. Ulm, Ulm, West Germany

Abstract

To avoid trivial compromises, most on-line statistical databases refuse to answer queries for statistics about small subgroups. Previous research discovered a powerful snooping tool, the tracker, with which the answers to these unanswerable queries are easily calculated. However, the extent of this threat was not clear, for no one had shown that finding a tracker is guaranteed to be easy. This paper gives a simple algorithm for finding a tracker when the maximum number of identical records is not too large. The number of queries required to find a tracker is at most Ο (log 2 S ) queries, where S is the number of distinct records possible. Experimental results show that the procedure often finds a tracker with just a few queries. The threat posed by trackers is therefore considerable.

Publisher

Association for Computing Machinery (ACM)

Subject

Information Systems

Reference16 articles.

1. Security in statistical databases for queries with small counts

2. DALENIUS T. Towards a methodology for statistical disclosure control. Statistisk Tidskrift i5 (1977) 429-444. DALENIUS T. Towards a methodology for statistical disclosure control. Statistisk Tidskrift i5 (1977) 429-444.

3. Database Security

4. Even Data Bases That Lie Can Be Compromised

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