Bisimulation through probabilistic testing
Information and Computation
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Protecting Respondents' Identities in Microdata Release
IEEE Transactions on Knowledge and Data Engineering
A Logical Model for Privacy Protection
ISC '01 Proceedings of the 4th International Conference on Information Security
k-anonymity: a model for protecting privacy
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Achieving k-anonymity privacy protection using generalization and suppression
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Reasoning about Uncertainty
\ell -Diversity: Privacy Beyond \kappa -Anonymity
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
L-diversity: Privacy beyond k-anonymity
ACM Transactions on Knowledge Discovery from Data (TKDD)
An epistemic framework for privacy protection in database linking
Data & Knowledge Engineering
The cost of privacy: destruction of data-mining utility in anonymized data publishing
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
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As privacy-preserving data publication has received much attention in recent years, a common technique for protecting privacy is to release the data in a sanitized form. To assess the effect of sanitization methods, several data privacy criteria have been proposed. Different privacy criteria can be employed by a data manager to prevent different attacks, since it is unlikely that a single criterion can meet the challenges posed by all possible attacks. Thus, a natural requirement of data management is to have a flexible language for expressing different privacy constraints. Furthermore, the purpose of data analysis is to discover general knowledge from the data. Hence, we also need a formalism to represent the discovered knowledge. The purpose of the paper is to provide such a formal language based on probabilistic hybrid logic, which is a combination of quantitative uncertainty logic and basic hybrid logic with a satisfaction operator. The main contribution of the work is twofold. On one hand, the logic provides a common ground to express and compare existing privacy criteria. On the other hand, the uniform framework can meet the specification needs of combining new criteria as well as existing ones.