Paper Info
Title
Modeling and Integrating Background Knowledge in Data Anonymization
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.
Year
DOI
Venue
2009
10.1109/ICDE.2009.86
ICDE
Keywords
Field
DocType
privacy requirement,data publishing,data publisher,probabilistic background knowledge,data anonymization,privacy protection,integrating background knowledge,general framework,model background knowledge,privacy model,data security,bayesian inference,kernel,probability distribution,anonymity,publishing,mathematical model,privacy,data mining,estimation,estimation theory,data privacy,computational modeling,kernel estimation
Data mining,Data security,Descriptive knowledge,Computer science,Data anonymization,Data publishing,Anonymity,Adversary,Probabilistic logic,Information privacy,Database
Conference
ISSN
Citations 
PageRank 
1084-4627
32
0.99
References 
Authors
44
3
Name
Order
Citations
PageRank
Tiancheng Li1157761.01
Ninghui Li25863305.02
Jian Zhang322212.92