Disclosure limitation of sensitive rules

Data products (macrodata or tabular data and micro-data or raw data records), are designed to inform public or business policy, and research or public information. Securing these products against unauthorized accesses has been a long-term goal of the database security research community and the government statistical agencies. Solutions to this problem require combining several techniques and mechanisms. Recent advances in data mining and machine learning algorithms have, however, increased the security risks one may incur when releasing data for mining from outside parties. Issues related to data mining and security have been recognized and investigated only recently. This paper deals with the problem of limiting disclosure of sensitive rules. In particular it is attempted to selectively hide some frequent itemsets from large databases with as little as possible impact on other non-sensitive frequent itemsets. Frequent itemsets are sets of items that appear in the database “frequently enough” and identifying them is usually the first step toward association/correlation rule or sequential pattern mining. Experimental results are presented along with some theoretical issues related to this problem