Disclosure Analysis and Control in Statistical Databases

Disclosure analysis and control are critical to protect sensitive information in statistical databases when some statistical moments are released. A generic question in disclosure analysis is whether a data snooper can deduce any sensitive information from available statistical moments. To address this question, the authors consider various types of possible disclosure based on the exact bounds that a snooper can infer about any protected moments from available statistical moments. They focus on protecting static moments in two-dimensional tables and obtain the following results. For each type of disclosure, they reveal the distribution patterns of protected moments that are subject to disclosure. Based on the disclosure patterns, they design efficient algorithms to discover all protected moments that are subject to disclosure.