Decomposition: Privacy Preservation for Multiple Sensitive Attributes

Authors:
Yang Ye;Yu Liu;Chi Wang;Dapeng Lv;Jianhua Feng
Affiliations:
Institute for Theoretical Computer Science, Tsinghua University, Beijing, P.R.China 100084;Department of Computer Science, Tsinghua University, Beijing, P.R.China 100084;Department of Computer Science, Tsinghua University, Beijing, P.R.China 100084;Department of Computer Science, Tsinghua University, Beijing, P.R.China 100084;Department of Computer Science, Tsinghua University, Beijing, P.R.China 100084
Venue:
DASFAA '09 Proceedings of the 14th International Conference on Database Systems for Advanced Applications
Year:
2009

Citing 5
Cited 1

Achieving k-anonymity privacy protection using generalization and suppression

International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Incognito: efficient full-domain K-anonymity

Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Mondrian Multidimensional K-Anonymity

ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
\ell -Diversity: Privacy Beyond \kappa -Anonymity

ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
BSGI: An Effective Algorithm towards Stronger l-Diversity

DEXA '08 Proceedings of the 19th international conference on Database and Expert Systems Applications

Privacy beyond single sensitive attribute

DEXA'11 Proceedings of the 22nd international conference on Database and expert systems applications - Volume Part I

Quantified Score

Hi-index	0.00

Visualization

Abstract

Aiming at ensuring privacy preservation in personal data publishing, the topic of anonymization has been intensively studied in recent years. However, existing anonymization techniques all assume each tuple in the microdata table contains one single sensitive attribute (the SSA case), while none paid attention to the case of multiple sensitive attributes in a tuple (the MSA case). In this paper, we conduct the pioneering study on the MSA case, and propose a new framework, decomposition, to tackle privacy preservation in the MSA case.