MultiRelational k-Anonymity

1 January 2007

conference paper
Published by Institute of Electrical and Electronics Engineers (IEEE)

No. 10636382,p. 1417-1421
https://doi.org/10.1109/icde.2007.369025

Abstract

k-anonymity protects privacy by ensuring that data cannot be linked to a single individual. In a k-anonymous dataset, any identifying information occurs in at least k tuples. Much research has been done to modify a single table dataset to satisfy anonymity constraints. This paper extends the definitions of k-anonymity to multiple relations and shows that previously proposed methodologies either fail to protect privacy, or overly reduce the utility of the data, in a multiple relation setting. A new clustering algorithm is proposed to achieve multirelational anonymity.

Keywords

This publication has 12 references indexed in Scilit:

Achieving anonymity via clustering
Published by Association for Computing Machinery (ACM) ,2006
L-diversity: privacy beyond k-anonymity
Published by Institute of Electrical and Electronics Engineers (IEEE) ,2006
Ordinal, Continuous and Heterogeneous k-Anonymity Through Microaggregation
Data Mining and Knowledge Discovery, 2005
Incognito
Published by Association for Computing Machinery (ACM) ,2005
Data Privacy through Optimal k-Anonymization
Published by Institute of Electrical and Electronics Engineers (IEEE) ,2005
ACHIEVING k-ANONYMITY PRIVACY PROTECTION USING GENERALIZATION AND SUPPRESSION
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 2002
k-ANONYMITY: A MODEL FOR PROTECTING PRIVACY
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 2002
Transforming data to satisfy privacy constraints
Published by Association for Computing Machinery (ACM) ,2002
Protecting respondents identities in microdata release
IEEE Transactions on Knowledge and Data Engineering, 2001
Using Boolean reasoning to anonymize databases
Artificial Intelligence in Medicine, 1999