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Privacy preserving serial data publishing by role composition
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Source
Proceedings of the VLDB Endowment archive
Volume 1 ,  Issue 1  (August 2008) table of contents
SESSION: Privacy preservation table of contents
Pages 845-856  
Year of Publication: 2008
ISSN:2150-8097
Authors
Yingyi Bu  The Chinese University of Hong Kong
Ada Wai Chee Fu  The Chinese University of Hong Kong
Raymond Chi Wing Wong  Hong Kong University of Science and Technology
Lei Chen  Hong Kong University of Science and Technology
Jiuyong Li  University of South Australia
Publisher
Bibliometrics
Downloads (6 Weeks): 11,   Downloads (12 Months): 61,   Citation Count: 2
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ABSTRACT

Previous works about privacy preserving serial data publishing on dynamic databases have relied on unrealistic assumptions of the nature of dynamic databases. In many applications, some sensitive values changes freely while others never change. For example, in medical applications, the disease attribute changes with time when patients recover from one disease and develop another disease. However, patients do not recover from some diseases such as HIV. We call such diseases permanent sensitive values. To the best of our knowledge, none of the existing solutions handle these realistic issues. We propose a novel anonymization approach called HD-composition to solve the above problems. Extensive experiments with real data confirm our theoretical results.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
1
J. Byun, Y. Sohn, E. Bertino, and N. Li. Secure anonymization for incremental datasets. In Secure Data Management, pages 48--63, 2006.
2
 
3
 
4
K. LeFevre, D. DeWitt, and R. Ramakrishnan. Multidimensional k-anonymity. In M. Technical Report 1521, University of Wisconsin, 2005.
5
 
6
J. Li, R. Wong, Ada, and J. Pei. Achieving k-anonymity by clustering in attribute hierarchical structures. In DaWaK, pages 405--416, 2006.
 
7
N. Li and T. Li. t-closeness: Privacy beyond k-anonymity and l-diversity. In ICDE, 2007.
 
8
 
9
D. J. Martin, D. Kifer, A. Machanavajjhala, and J. Gehrke. Worst-case background knowledge for privacy-preserving data publishing. In ICDE, 2007.
10
 
11
 
12
P. Samarati and L. Sweeney. Protecting privacy when disclosing information: k-anonymity and its enforcement through generalization and suppression, unpublished manuscript. In unpublished, 1998.
 
13
L. Sweeney. Weaving technology and policy together to maintain confidentiality. Journal of Law, Medicine and Ethics, 25(2--3): 98--110, 1997.
 
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Collaborative Colleagues:
Yingyi Bu: colleagues
Ada Wai Chee Fu: colleagues
Raymond Chi Wing Wong: colleagues
Lei Chen: colleagues
Jiuyong Li: colleagues