Personalized privacy preservation
Top Cited Papers
- 27 June 2006
- proceedings article
- Published by Association for Computing Machinery (ACM)
- p. 229-240
- https://doi.org/10.1145/1142473.1142500
Abstract
We study generalization for preserving privacy in publication of sensitive data. The existing methods focus on a universal approach that exerts the same amount of preservation for all persons, with- out catering for their concrete needs. The consequence is that we may be offering insufficient protection to a subset of people, while applying excessive privacy control to another subset. Motivated by this, we present a new generalization framework based on the concept of personalized anonymity. Our technique performs the minimum generalization for satisfying everybody's requirements, and thus, retains the largest amount of information from the microdata. We carry out a careful theoretical study that leads to valuable insight into the behavior of alternative solutions. In particular, our analysis mathematically reveals the circumstances where the previous work fails to protect privacy, and establishes the superiority of the proposed solutions. The theoretical findings are verified with extensive experiments.Keywords
This publication has 16 references indexed in Scilit:
- Handicapping attacker's confidence: an alternative to k-anonymizationKnowledge and Information Systems, 2006
- IncognitoPublished by Association for Computing Machinery (ACM) ,2005
- On the complexity of optimal K-anonymityPublished by Association for Computing Machinery (ACM) ,2004
- Anonymizing TablesPublished by Springer Nature ,2004
- Revealing information while preserving privacyPublished by Association for Computing Machinery (ACM) ,2003
- ACHIEVING k-ANONYMITY PRIVACY PROTECTION USING GENERALIZATION AND SUPPRESSIONInternational Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 2002
- Transforming data to satisfy privacy constraintsPublished by Association for Computing Machinery (ACM) ,2002
- Protecting respondents identities in microdata releaseIEEE Transactions on Knowledge and Data Engineering, 2001
- Mining quantitative association rules in large relational tablesPublished by Association for Computing Machinery (ACM) ,1996
- Security-control methods for statistical databases: a comparative studyACM Computing Surveys, 1989