Paper Info
Title
Inference Analysis in Privacy-Preserving Data Re-publishing
Abstract
Privacy-Preserving Data Re-publishing (PPDR) deals with publishing microdata in dynamic scenarios. Due to privacy concerns, data must be disguised before being published. Research in privacy-preserving data publishing (PPDP) has proposed many such methods on static data. In PPDR, multiple appeared records can be used to infer private information of other records. Therefore, inference channels exist among different releases. To understand the privacy property of data re-publishing, we need to analyze the impact of these inference channels. Previous studies show such analysis when data are updated or disguised in special ways, however, no general method has been proposed. Using the Maximum Entropy Modeling method, we have developed a general solution. Our method can conduct inference analysis when data are arbitrarily updated or arbitrarily disguised using either generalization or bucketization, two most common data disguise methods in PPDR. Through analysis and experiments, we demonstrate the advantage and the effectiveness of our method.
Year
DOI
Venue
2008
10.1109/ICDM.2008.118
ICDM
Keywords
Field
DocType
general solution,privacy-preserving data publishing,privacy concern,inference analysis,privacy property,maximum entropy modeling method,static data,privacy-preserving data re-publishing,common data,general method,inference channel,generalization,maximum entropy model,data privacy,mathematical model,diabetes,entropy,private information
Data mining,Computer science,Inference,Communication channel,Artificial intelligence,Microdata (HTML),Data publishing,Principle of maximum entropy,Publishing,Information privacy,Private information retrieval,Machine learning
Conference
ISSN
Citations 
PageRank 
1550-4786
6
0.52
References 
Authors
11
4
Name
Order
Citations
PageRank
Guan Wang149635.22
Zutao Zhu2774.56
wenliang du34906241.77
Zhouxuan Teng4976.41