The semantic discrimination rate metric for privacy measurements which questions the benefit of t-closeness over l-diversity

Louis Philippe Sondeck; Maryline Laurent; Vincent Frey

doi:10.5220/0006418002850294

The semantic discrimination rate metric for privacy measurements which questions the benefit of t-closeness over l-diversity

Louis Philippe Sondeck
, Maryline Laurent
, Vincent Frey

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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 tcloseness can give lower privacy protection than l-diversity.

Original language	English
Title of host publication	SECRYPT
Editors	Pierangela Samarati, Mohammad S. Obaidat, Enrique Cabello
Publisher	SciTePress
Pages	285-294
Number of pages	10
ISBN (Electronic)	9789897582592
DOIs	https://doi.org/10.5220/0006418002850294
Publication status	Published - 1 Jan 2017
Externally published	Yes
Event	14th International Joint Conference on e-Business and Telecommunications, ICETE 2017 - Madrid, Spain Duration: 24 Jul 2017 → 26 Jul 2017

Publication series

Name	ICETE 2017 - Proceedings of the 14th International Joint Conference on e-Business and Telecommunications
Volume	4

Conference

Conference	14th International Joint Conference on e-Business and Telecommunications, ICETE 2017
Country/Territory	Spain
City	Madrid
Period	24/07/17 → 26/07/17

Keywords

Anonymity Metric
Discrimination Rate
Identifiability
K-anonymity t-closeness
L-diversity
Semantic Discrimination Rate

Access to Document

10.5220/0006418002850294

Cite this

Sondeck, L. P., Laurent, M., & Frey, V. (2017). The semantic discrimination rate metric for privacy measurements which questions the benefit of t-closeness over l-diversity. In P. Samarati, M. S. Obaidat, & E. Cabello (Eds.), SECRYPT (pp. 285-294). (ICETE 2017 - Proceedings of the 14th International Joint Conference on e-Business and Telecommunications; Vol. 4). SciTePress. https://doi.org/10.5220/0006418002850294

Sondeck, Louis Philippe ; Laurent, Maryline ; Frey, Vincent. / The semantic discrimination rate metric for privacy measurements which questions the benefit of t-closeness over l-diversity. SECRYPT. editor / Pierangela Samarati ; Mohammad S. Obaidat ; Enrique Cabello. SciTePress, 2017. pp. 285-294 (ICETE 2017 - Proceedings of the 14th International Joint Conference on e-Business and Telecommunications).

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title = "The semantic discrimination rate metric for privacy measurements which questions the benefit of t-closeness over l-diversity",

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 tcloseness can give lower privacy protection than l-diversity.",

keywords = "Anonymity Metric, Discrimination Rate, Identifiability, K-anonymity t-closeness, L-diversity, Semantic Discrimination Rate",

author = "Sondeck, \{Louis Philippe\} and Maryline Laurent and Vincent Frey",

note = "Publisher Copyright: Copyright {\textcopyright} 2017 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved.; 14th International Joint Conference on e-Business and Telecommunications, ICETE 2017 ; Conference date: 24-07-2017 Through 26-07-2017",

year = "2017",

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doi = "10.5220/0006418002850294",

language = "English",

series = "ICETE 2017 - Proceedings of the 14th International Joint Conference on e-Business and Telecommunications",

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Sondeck, LP, Laurent, M & Frey, V 2017, The semantic discrimination rate metric for privacy measurements which questions the benefit of t-closeness over l-diversity. in P Samarati, MS Obaidat & E Cabello (eds), SECRYPT. ICETE 2017 - Proceedings of the 14th International Joint Conference on e-Business and Telecommunications, vol. 4, SciTePress, pp. 285-294, 14th International Joint Conference on e-Business and Telecommunications, ICETE 2017, Madrid, Spain, 24/07/17. https://doi.org/10.5220/0006418002850294

The semantic discrimination rate metric for privacy measurements which questions the benefit of t-closeness over l-diversity. / Sondeck, Louis Philippe; Laurent, Maryline; Frey, Vincent.
SECRYPT. ed. / Pierangela Samarati; Mohammad S. Obaidat; Enrique Cabello. SciTePress, 2017. p. 285-294 (ICETE 2017 - Proceedings of the 14th International Joint Conference on e-Business and Telecommunications; Vol. 4).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - 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.

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KW - Discrimination Rate

KW - Identifiability

KW - K-anonymity t-closeness

KW - L-diversity

KW - Semantic Discrimination Rate

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Sondeck LP, Laurent M, Frey V. The semantic discrimination rate metric for privacy measurements which questions the benefit of t-closeness over l-diversity. In Samarati P, Obaidat MS, Cabello E, editors, SECRYPT. SciTePress. 2017. p. 285-294. (ICETE 2017 - Proceedings of the 14th International Joint Conference on e-Business and Telecommunications). doi: 10.5220/0006418002850294

The semantic discrimination rate metric for privacy measurements which questions the benefit of t-closeness over l-diversity

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Keywords

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