Protecting Respondents' Identities in Microdata Release
IEEE Transactions on Knowledge and Data Engineering
Incognito: efficient full-domain K-anonymity
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
On k-anonymity and the curse of dimensionality
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Approximate algorithms for K-anonymity
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Privacy-preserving data publishing: A survey of recent developments
ACM Computing Surveys (CSUR)
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The publishing of data with privacy guarantees is a task typically performed by a data curator who is expected to provide guarantees for the data he publishes in quantitative fashion, via a privacy criterion (e.g., k-anonymity, l-diversity). The anonymization of data is typically performed off-line. In this paper, we provide algorithmic tools that facilitate the negotiation for the anonymization scheme of a data set in user time. Our method takes as input a set of user constraints for (i) suppression, (ii) generalization and (iii) a privacy criterion (k-anonymity, l-diversity) and returns (a) either an anonymization scheme that fulfils these constraints or, (b) three approximations to the user request based on the idea of keeping the two of the three values of the user input fixed and finding the closest possible approximation for the third parameter. The proposed algorithm involves precomputing suitable histograms for all the different anonymization schemes that a global recoding method can follow. This allows computing exact answers extremely fast (in the order of few milliseconds).