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ICDE
2009
IEEE

Modeling and Integrating Background Knowledge in Data Anonymization

15 years 3 months ago
Modeling and Integrating Background Knowledge in Data Anonymization
Recent work has shown the importance of considering the adversary’s background knowledge when reasoning about privacy in data publishing. However, it is very difficult for the data publisher to know exactly the adversary’s background knowledge. Existing work cannot satisfactorily model background knowledge and reason about privacy in the presence of such knowledge. This paper presents a general framework for modeling the adversary’s background knowledge using kernel estimation methods. This framework subsumes different types of knowledge (e.g., negative association rules) that can be mined from the data. Under this framework, we reason about privacy using Bayesian inference techniques and propose the skyline (B, t)-privacy model, which allows the data publisher to enforce privacy requirements to protect the data against adversaries with different levels of background knowledge. Through an extensive set of experiments, we show the effects of probabilistic background knowledge in...
Tiancheng Li, Ninghui Li, Jian Zhang
Added 20 Dec 2008
Updated 30 Oct 2009
Type Conference
Year 2009
Where ICDE
Authors Tiancheng Li, Ninghui Li, Jian Zhang
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