Authors:
Louis Philippe Sondeck
1
;
Maryline Laurent
2
and
Vincent Frey
3
Affiliations:
1
Orange Labs and Telecom SudParis, France
;
2
Telecom SudParis, Paris-Saclay University and CNRS, France
;
3
Orange Labs, France
Keyword(s):
Anonymity Metric, Semantic Discrimination Rate, Discrimination Rate, Identifiability, k-anonymity, t-closeness, l-diversity.
Related
Ontology
Subjects/Areas/Topics:
Data and Application Security and Privacy
;
Data Engineering
;
Data Integrity
;
Data Protection
;
Database Security and Privacy
;
Databases and Data Security
;
Information and Systems Security
;
Information Assurance
;
Privacy
;
Privacy Enhancing Technologies
;
Risk Assessment
;
Security and Privacy for Big Data
;
Security in Information Systems
Abstract:
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 t-closeness
can give lower privacy protection than l-diversity.