On the Damage and Compensation of Privacy Leakage
Dawei Wang, Churn‐Jung Liau, Tsan‐sheng Hsu, Jeremy K.-P. Chen · Kluwer Academic Publishers eBooks · 2006
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. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.