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The Semantic Discrimination Rate Metric for Privacy Measurements which Questions the Benefit of t-closeness over l-diversity
2017
Proceedings of the 14th International Joint Conference on e-Business and Telecommunications
After a brief description of k-anonymity, l-diversity and t-closeness techniques, the paper presents the Discrimination Rate (DR) as a new metric based on information theory for measuring the privacy level of any anonymization technique. As far as we know, the DR is the first approach supporting fine grained privacy measurement down to attribute's values. Increased with the semantic dimension, the resulting semantic DR (SeDR) enables to: (1) tackle anonymity measurements from the attacker's
doi:10.5220/0006418002850294
dblp:conf/secrypt/SondeckLF17
fatcat:sgzbjx2kyrfvtmazqx4oabrzlq