Security of random data perturbation methods
ACM Transactions on Database Systems (TODS)
Privacy-preserving data mining
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
On the design and quantification of privacy preserving data mining algorithms
PODS '01 Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Achieving k-anonymity privacy protection using generalization and suppression
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Data Privacy through Optimal k-Anonymization
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Incognito: efficient full-domain K-anonymity
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
On Unifying Privacy and Uncertain Data Models
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Using mahalanobis distance-based record linkage for disclosure risk assessment
PSD'06 Proceedings of the 2006 CENEX-SDC project international conference on Privacy in Statistical Databases
An inequality on guessing and its application to sequential decoding
IEEE Transactions on Information Theory
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This paper introduces the `guessing anonymity,' a definition of privacy for noise perturbation methods. This definition captures the difficulty of linking identity to a sanitized record using publicly available information. Importantly, this definition leads to analytical expressions that bound data privacy as a function of the noise perturbation parameters. Using these bounds, we can formulate optimization problems to describe the feasible tradeoffs between data distortion and privacy, without exhaustively searching the noise parameter space. This work addresses an important shortcoming of noise perturbation methods, by providing them with an intuitive definition of privacy analogous to the definition used in k-anonymity, and an analytical means for selecting parameters to achieve a desired level of privacy. At the same time, our work maintains the appealing aspects of noise perturbation methods, which have made them popular both in practice and as a subject of academic research.