On Computational Anonymity

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.