Natural change in dynamic databases as a deterrent to compromise by trackers

There has been considerable research in database security and inference control; however, that research deals primarily with statistical databases. Although some of the mechanisms proposed to deter compromise artificially introduce dynamicism into these otherwise static databases, very little effort has been devoted to the area of natural dynamics. The purpose of the work presented is to determine a deterrent value for natural change in dynamic databases. More specifically, the authors investigate how the changing value of an attribute prevents the inference of the confidential value of that attribute for any particular record. Assuming a restricted query set size, they calculate bounds of deterrence to compromise by the individual, general, and double trackers.<
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