A Comparative Study of Query-Set Size and Fixed-Data Perturbation as Two Techniques to Secure Statistical Databases
A Statistical DataBase (SDB) is a database that contains a large number of individual sensitive records, but is intended to supply only statistical summary information to its users. A SDB suffers from the inference problem, a way to infer or derive sensitive data from non-sensitive data. In this paper, two security techniques of SDBs, query-set size and fixed-data perturbation are selected to review and compare each other. As a result, no one is a perfect solution for the inference problem.