Generalizing data to provide anonymity when disclosing information (abstract)
PODS '98 Proceedings of the seventeenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Protecting Respondents' Identities in Microdata Release
IEEE Transactions on Knowledge and Data Engineering
k-anonymity: a model for protecting privacy
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Transforming data to satisfy privacy constraints
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
\ell -Diversity: Privacy Beyond \kappa -Anonymity
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
(α, k)-anonymity: an enhanced k-anonymity model for privacy preserving data publishing
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
The boundary between privacy and utility in data publishing
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Attacks on privacy and deFinetti's theorem
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Privacy-preserving publishing microdata with full functional dependencies
Data & Knowledge Engineering
Design by example for SQL table definitions with functional dependencies
The VLDB Journal — The International Journal on Very Large Data Bases
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We study the privacy threat by publishing data that contains full functional dependencies (FFDs). We show that the cross-attribute correlations by FFDs can bring potential vulnerability to privacy. Unfortunately, none of the existing anonymization principles can effectively prevent against the FFD-based privacy attack. In this paper, we formalize the FFD-based privacy attack, define the privacy model (d, l)-inference to combat the FFD-based attack, and design robust anonymization algorithm that achieves (d, l)-inference. The efficiency and effectiveness of our approach are demonstrated by the empirical study.