Inference analysis in privacy-preserving data re-publishing

Guan Wang, Zutao Zhu, Wenliang Du, Zhouxuan Teng

Research output: Chapter in Book/Entry/PoemConference contribution

13 Scopus citations


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.

Original languageEnglish (US)
Title of host publicationProceedings - 8th IEEE International Conference on Data Mining, ICDM 2008
Number of pages6
StatePublished - 2008
Event8th IEEE International Conference on Data Mining, ICDM 2008 - Pisa, Italy
Duration: Dec 15 2008Dec 19 2008

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)1550-4786


Other8th IEEE International Conference on Data Mining, ICDM 2008

ASJC Scopus subject areas

  • General Engineering


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