Privacy Skyline: Privacy with Multidimensional Adversarial Knowledge

Privacy is an important issue in data publishing. Many organizations distribute non-aggregate personal data for research, and they must take steps to ensure that an adversary cannot predict sensitive information pertaining to individuals with high confidence. This problem is further complicated by the fact that, in addition to the published data, the adversary may also have access to other resources (e.g., public records and social networks relating individuals), which we call external knowledge. A robust privacy criterion should take this external knowledge into consideration. In this paper, we first describe a general framework for reasoning about privacy in the presence of external knowledge. Within this framework, we propose a novel multidimensional approach to quantifying an adversary’s external knowledge. This approach allows the publishing organization to investigate privacy threats and enforce privacy requirements in the presence of various types and amounts of external know...

• Type: Conference
• Year: 2007
• Where: VLDB
• Authors: Bee-Chung Chen, Raghu Ramakrishnan, Kristen LeFevre
• Keywords: Database, External Knowledge, Privacy, Privacy Criterion, VLDB 2007