Modeling and Integrating Background Knowledge in Data Anonymization

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Author

Tiancheng Li; Ninghui Li; Jian Zhang

Tech report number

CERIAS TR 2009-16

Entry type

inproceedings

Abstract

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 data anonymization and the effectiveness of our approach in both privacy protection and utility preservation.

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Date

2009 – 1 – 1

Booktitle

International Conference on Data Engineering (ICDE), 2009

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Publication Date

2009-01-01

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@Inproceedings{ Li,
	title = "Modeling and Integrating Background Knowledge in Data Anonymization",
	author = "Tiancheng Li; Ninghui Li; Jian Zhang",
	year = "2009",
	month = "1",
	booktitle = "International Conference on Data Engineering (ICDE), 2009",
	day = "1",
	abstract = "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 data anonymization and the effectiveness of our approach in both privacy protection and utility preservation.",
}