We address issues related to the protection of private information in Online Analytical Processing (OLAP) systems, where a major privacy concern is the adversarial inference of private information from OLAP query answers. Most previous work on privacy-preserving OLAP focuses on a single aggregate function and/or addresses only exact disclosure, which eliminates from consideration an important class of privacy breaches where partial information, but not exact values, of private data is disclosed (i.e., partial disclosure). We address privacy protection against both exact and partial disclosure in OLAP systems with mixed aggregate functions. In particular, we propose an information-theoretic inference control approach that supports a combination of common aggregate functions (e.g., COUNT, SUM, MIN, MAX, and MEDIAN) and guarantees the level of privacy disclosure not to exceed thresholds predetermined by the data owners. We demonstrate that our approach is efficient and can be implemented in existing OLAP systems with little modification. It also satisfies the simulatable auditing model and leaks no private information through query rejections. Through performance analysis, we show that compared with previous approaches, our approach provides more effective privacy protection while maintaining a higher level of query-answer availability.
['Nan Zhang', 'Wei Zhao']
Privacy-Preserving OLAP: An Information-Theoretic Approach