Abstract
In this paper we study the problem of anonymity in multi-instance (MI) micro-data publication. The classical k-anonymity approach is shown to be insufficient and/or inappropriate for MI databases. Thus, it is extended to MI databases, resulting in a more general setting of MI k-anonymity. We show that MI k-anonymity problem is NP-Hard and the attack model for MI databases is different from that of single-instance databases. We make an observation that the introduced MI k-anonymity is not a strong privacy guarantee when anonymity sets are highly unbalanced with respect to instance counts. To this end a new anonymity principle, called p-certainty, which is unique to MI case is introduced. A clustering algorithms solving the p-certainty anonymity principle is developed and experimentally evaluated.
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Abul, O. (2013). Anonymity in Multi-Instance Micro-Data Publication. In: Gelenbe, E., Lent, R. (eds) Information Sciences and Systems 2013. Lecture Notes in Electrical Engineering, vol 264. Springer, Cham. https://doi.org/10.1007/978-3-319-01604-7_32
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DOI: https://doi.org/10.1007/978-3-319-01604-7_32
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