The Semantic Discrimination Rate Metric for Privacy Measurements which Questions the Benefit of t-closeness over l-diversity

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 perspective, (2) prove that tcloseness can give lower privacy protection than l-diversity.

[1]  Sang Joon Kim,et al.  A Mathematical Theory of Communication , 2006 .

[2]  Vicenç Torra,et al.  Towards Semantic Microaggregation of Categorical Data for Confidential Documents , 2010, MDAI.

[3]  Josep Domingo-Ferrer,et al.  From t-Closeness-Like Privacy to Postrandomization via Information Theory , 2010, IEEE Transactions on Knowledge and Data Engineering.

[4]  Andreas Haeberlen,et al.  Differential Privacy: An Economic Method for Choosing Epsilon , 2014, 2014 IEEE 27th Computer Security Foundations Symposium.

[5]  Chris Clifton,et al.  How Much Is Enough? Choosing ε for Differential Privacy , 2011, ISC.

[6]  ASHWIN MACHANAVAJJHALA,et al.  L-diversity: privacy beyond k-anonymity , 2006, 22nd International Conference on Data Engineering (ICDE'06).

[7]  Ali Makhdoumi,et al.  Privacy-utility tradeoff under statistical uncertainty , 2013, 2013 51st Annual Allerton Conference on Communication, Control, and Computing (Allerton).

[8]  Pierangela Samarati,et al.  Protecting Respondents' Identities in Microdata Release , 2001, IEEE Trans. Knowl. Data Eng..

[9]  Vincent Frey,et al.  Discrimination rate: an attribute-centric metric to measure privacy , 2017, Ann. des Télécommunications.

[10]  H. Vincent Poor,et al.  Utility-Privacy Tradeoffs in Databases: An Information-Theoretic Approach , 2011, IEEE Transactions on Information Forensics and Security.

[11]  Ninghui Li,et al.  t-Closeness: Privacy Beyond k-Anonymity and l-Diversity , 2007, 2007 IEEE 23rd International Conference on Data Engineering.

[12]  Vicenç Torra,et al.  Semantic Microaggregation for the Anonymization of Query Logs , 2010, Privacy in Statistical Databases.

[13]  Nina Taft,et al.  How to hide the elephant- or the donkey- in the room: Practical privacy against statistical inference for large data , 2013, 2013 IEEE Global Conference on Signal and Information Processing.

[14]  Josep Domingo-Ferrer,et al.  A Critique of k-Anonymity and Some of Its Enhancements , 2008, 2008 Third International Conference on Availability, Reliability and Security.