Database Audit Workload Prioritization via Game Theory
Abstract
The quantity of personal data that is collected, stored, and subsequently processed continues to grow rapidly. Given its sensitivity, ensuring privacy protections has become a necessary component of database management. To enhance protection, a number of mechanisms have been developed, such as audit logging and alert triggers, which notify administrators about suspicious activities. However, this approach is limited. First, the volume of alerts is often substantially greater than the auditing capabilities of organizations. Second, strategic attackers can attempt to disguise their actions or carefully choose targets, thus hide illicit activities. In this article, we introduce an auditing approach that accounts for adversarial behavior by (1) prioritizing the order in which types of alerts are investigated and (2) providing an upper bound on how much resource to allocate for each type.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jun 10, 2019
- Source ID
- 10.1145/3323924
Entities
People
- Aron Laszka
- Bo Li
- Bradley Malin
- Chao Yan
- Daniel Fabbri
- Yevgeniy Vorobeychik
Organizations
- Army Research Office
- National Institutes of Health
- National Science Foundation
- Office of Naval Research
- University of Houston
- University of Illinois Urbana–Champaign
- Vanderbilt University
- Washington University in St. Louis