Combinatorics: set systems, hypergraphs, families of vectors, and combinatorial probability
Combinatorics: set systems, hypergraphs, families of vectors, and combinatorial probability
Crowds: anonymity for Web transactions
ACM Transactions on Information and System Security (TISSEC)
Protecting Respondents' Identities in Microdata Release
IEEE Transactions on Knowledge and Data Engineering
k-anonymity: a model for protecting privacy
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Achieving k-anonymity privacy protection using generalization and suppression
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Ordinal, Continuous and Heterogeneous k-Anonymity Through Microaggregation
Data Mining and Knowledge Discovery
User-private information retrieval based on a peer-to-peer community
Data & Knowledge Engineering
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The concern of data privacy is to mask data so that they can be transferred to untrusted third parties without leaking confidential individual information. In this work we distinguish between theoretical anonymity and computational anonymity. We present a relaxation of k-anonymity, called (k,l)-anonymity, which makes sense when it can be assumed that the knowledge of an adversary is limited. (k,l)-Anonymity can also be regarded as a quantification of the anonymity in terms of the adversary's limitations. Combinatorics, or more precisely, hypergraphs, are used to represent the anonymity relations in a (k,l)-anonymous table. Finally, we present an algorithm for the (k,l)-anonymization of tables.