Protecting Respondents' Identities in Microdata Release
IEEE Transactions on Knowledge and Data Engineering
Transforming data to satisfy privacy constraints
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Probabilistic Information Loss Measures in Confidentiality Protection of Continuous Microdata
Data Mining and Knowledge Discovery
Utility-based anonymization using local recoding
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
High-rate quantization and transform coding with side information at the decoder
Signal Processing - Special section: Distributed source coding
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
From t-Closeness to PRAM and Noise Addition Via Information Theory
PSD '08 Proceedings of the UNESCO Chair in data privacy international conference on Privacy in Statistical Databases
How Protective Are Synthetic Data?
PSD '08 Proceedings of the UNESCO Chair in data privacy international conference on Privacy in Statistical Databases
Data Access in a Cyber World: Making Use of Cyberinfrastructure
Transactions on Data Privacy
Expressing privacy metrics as one-symbol information
Proceedings of the 2010 EDBT/ICDT Workshops
An information theoretic approach for privacy metrics
Transactions on Data Privacy
Testing software in age of data privacy: a balancing act
Proceedings of the 19th ACM SIGSOFT symposium and the 13th European conference on Foundations of software engineering
A comparison of two different types of online social network from a data privacy perspective
MDAI'11 Proceedings of the 8th international conference on Modeling decisions for artificial intelligence
Information fusion in data privacy: A survey
Information Fusion
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Before releasing anonymized microdata (individual data) it is essential to evaluate whether: i) their utility is high enough for their release to make sense; ii) the risk that the anonymized data result in disclosure of respondent identity or respondent attribute values is low enough. Utility and disclosure risk measures are used for the above evaluation, which normally lack a common theoretical framework allowing to trade off utility and risk in a consistent way. We explore in this paper the use of information-theoretic measures based on the notion of mutual information.