On the damage and compensation of privacy leakage

Abstract

A query on the distribution of a sensitive field within a selected population in a database can be submitted to the data center, and the answer to this query can leak private information, even though no identification information is provided. Inspired by decision theory, we present a quantitative model of the privacy protection problem in such a database query environment. In our model, the user information states are defined as classes of probability distributions on the set of possible confidential values. These states can be modified and refined by knowledge acquisition actions. The data confidentiality is guaranteed by ensuring that misusing private information is more costly than any possible gain. © 2004 Springer Science + Business Media, Inc.