Anatomy: Simple and effective privacy preservation
Refereed conference paper presented and published in conference proceedings


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AbstractThis paper presents a novel technique, anatomy, for publishing sensitive data. Anatomy releases all the quasi-identifier and sensitive values directly in two separate tables. Combined with a grouping Mechanism, this approach protects privacy, and captures a large amount of correlation in the microdata. We develop a linear-time algorithm for computing anatomized tables that obey the l-diversity Privacy requirement, and minimize the error of reconstructing the microdata. Extensive experiments confirm that our technique allows significantly more effective data analysis than the conventional publication method based on generalization. Specifically, anatomy permits aggregate reasoning with average error below 10%, which is lower than the error obtained from a generalized table by orders of magnitude. Copyright 2006 VLDB Endowment, ACM.
All Author(s) ListXiao X., Tao Y.
Name of Conference32nd International Conference on Very Large Data Bases, VLDB 2006
Start Date of Conference12/09/2006
End Date of Conference15/09/2006
Place of ConferenceSeoul
Country/Region of ConferenceSouth Korea
Detailed descriptionVLDB Endowment
Year2006
Month12
Day1
Pages139 - 150
ISBN1595933859
LanguagesEnglish-United Kingdom

Last updated on 2020-02-09 at 00:52