Security of statistical databases: multidimensional transformation
ACM Transactions on Database Systems (TODS)
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SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Protecting Respondents' Identities in Microdata Release
IEEE Transactions on Knowledge and Data Engineering
Practical Data-Oriented Microaggregation for Statistical Disclosure Control
IEEE Transactions on Knowledge and Data Engineering
Randomization in privacy preserving data mining
ACM SIGKDD Explorations Newsletter
k-anonymity: a model for protecting privacy
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
FOCS '95 Proceedings of the 36th Annual Symposium on Foundations of Computer Science
Using randomized response techniques for privacy-preserving data mining
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
IEEE Transactions on Knowledge and Data Engineering
Ordinal, Continuous and Heterogeneous k-Anonymity Through Microaggregation
Data Mining and Knowledge Discovery
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ICDEW '06 Proceedings of the 22nd International Conference on Data Engineering Workshops
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Communications of the ACM - Spam and the ongoing battle for the inbox
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Auditing and Inference Control in Statistical Databases
IEEE Transactions on Software Engineering
PSD'06 Proceedings of the 2006 CENEX-SDC project international conference on Privacy in Statistical Databases
An Anonymity Model Achievable Via Microaggregation
SDM '08 Proceedings of the 5th VLDB workshop on Secure Data Management
BNCOD 26 Proceedings of the 26th British National Conference on Databases: Dataspace: The Final Frontier
Editorial: Recent progress in database privacy
Data & Knowledge Engineering
The Functionality-Security-Privacy Game
MDAI '09 Proceedings of the 6th International Conference on Modeling Decisions for Artificial Intelligence
Perturbation of Numerical Confidential Data via Skew-t Distributions
Management Science
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Data Mining and Knowledge Discovery
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Database privacy is an ambiguous concept, whose meaning is usually context-dependent. We give a conceptual framework for technologies in that field in terms of three dimensions, depending on whose privacy is considered: i) respondent privacy (to avoid reidentification of patients or other individuals to whom the database records refer); ii) owner privacy (to ensure that the owner must not give away his dataset); and iii) user privacy (to preserve the privacy of queries submitted by a data user). Examples are given to clarify why these are three independent dimensions. Some of the pitfalls related to combining the privacy interests of respondents, owners and users are discussed. An assessment of database privacy technologies against the three dimensions is also included.