Expressing privacy metrics as one-symbol information
Proceedings of the 2010 EDBT/ICDT Workshops
Optimized query forgery for private information retrieval
IEEE Transactions on Information Theory
A privacy-preserving architecture for the semantic web based on tag suppression
TrustBus'10 Proceedings of the 7th international conference on Trust, privacy and security in digital business
SABRE: a Sensitive Attribute Bucketization and REdistribution framework for t-closeness
The VLDB Journal — The International Journal on Very Large Data Bases
An information theoretic approach for privacy metrics
Transactions on Data Privacy
Limiting disclosure of sensitive data in sequential releases of databases
Information Sciences: an International Journal
Kd-trees and the real disclosure risks of large statistical databases
Information Fusion
Publishing microdata with a robust privacy guarantee
Proceedings of the VLDB Endowment
A modification of the Lloyd algorithm for k-anonymous quantization
Information Sciences: an International Journal
Class-Restricted Clustering and Microperturbation for Data Privacy
Management Science
Information-Theoretic foundations of differential privacy
FPS'12 Proceedings of the 5th international conference on Foundations and Practice of Security
Measuring the privacy of user profiles in personalized information systems
Future Generation Computer Systems
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t-Closeness is a privacy model recently defined for data anonymization. A data set is said to satisfy t-closeness if, for each group of records sharing a combination of key attributes, the distance between the distribution of a confidential attribute in the group and the distribution of the attribute in the entire data set is no more than a threshold t. Here, we define a privacy measure in terms of information theory, similar to t-closeness. Then, we use the tools of that theory to show that our privacy measure can be achieved by the postrandomization method (PRAM) for masking in the discrete case, and by a form of noise addition in the general case.