A Tunable Measure for Information Leakage

Jiachun Liao; Oliver Kosut; Lalitha Sankar; Flavio P. Calmon

doi:10.1109/ISIT.2018.8437307

A Tunable Measure for Information Leakage

Jiachun Liao, Oliver Kosut, Lalitha Sankar, Flavio P. Calmon

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

24 Scopus citations

Abstract

A tunable measure for information leakage called maximal a-leakage is introduced. This measure quantifies the maximal gain of an adversary in refining a tilted version of its prior belief of any (potentially random) function of a dataset conditioning on a disclosed dataset. The choice of \alpha determines the specific adversarial action ranging from refining a belief for \alpha=1 to guessing the best posterior for \alpha=\infty, and for these extremal values this measure simplifies to mutual information (MI) and maximal leakage (MaxL), respectively. For all other \alpha this measure is shown to be the Arimoto channel capacity. Several properties of this measure are proven including: (i) quasi-convexity in the mapping between the original and disclosed datasets; (ii) data processing inequalities; and (iii) a composition property. A full version of this paper is in [1].

Original language	English (US)
Title of host publication	2018 IEEE International Symposium on Information Theory, ISIT 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	701-705
Number of pages	5
ISBN (Print)	9781538647806
DOIs	https://doi.org/10.1109/ISIT.2018.8437307
State	Published - Aug 15 2018
Event	2018 IEEE International Symposium on Information Theory, ISIT 2018 - Vail, United States Duration: Jun 17 2018 → Jun 22 2018

Publication series

Name	IEEE International Symposium on Information Theory - Proceedings
Volume	2018-June
ISSN (Print)	2157-8095

Other

Other	2018 IEEE International Symposium on Information Theory, ISIT 2018
Country/Territory	United States
City	Vail
Period	6/17/18 → 6/22/18

ASJC Scopus subject areas

Theoretical Computer Science
Information Systems
Modeling and Simulation
Applied Mathematics

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10.1109/ISIT.2018.8437307

Cite this

Liao, J., Kosut, O., Sankar, L., & Calmon, F. P. (2018). A Tunable Measure for Information Leakage. In 2018 IEEE International Symposium on Information Theory, ISIT 2018 (pp. 701-705). Article 8437307 (IEEE International Symposium on Information Theory - Proceedings; Vol. 2018-June). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISIT.2018.8437307

A Tunable Measure for Information Leakage. / Liao, Jiachun; Kosut, Oliver ; Sankar, Lalitha et al.
2018 IEEE International Symposium on Information Theory, ISIT 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 701-705 8437307 (IEEE International Symposium on Information Theory - Proceedings; Vol. 2018-June).

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

Liao, J, Kosut, O , Sankar, L & Calmon, FP 2018, A Tunable Measure for Information Leakage. in 2018 IEEE International Symposium on Information Theory, ISIT 2018., 8437307, IEEE International Symposium on Information Theory - Proceedings, vol. 2018-June, Institute of Electrical and Electronics Engineers Inc., pp. 701-705, 2018 IEEE International Symposium on Information Theory, ISIT 2018, Vail, United States, 6/17/18. https://doi.org/10.1109/ISIT.2018.8437307

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abstract = "A tunable measure for information leakage called maximal a-leakage is introduced. This measure quantifies the maximal gain of an adversary in refining a tilted version of its prior belief of any (potentially random) function of a dataset conditioning on a disclosed dataset. The choice of \alpha determines the specific adversarial action ranging from refining a belief for \alpha=1 to guessing the best posterior for \alpha=\infty, and for these extremal values this measure simplifies to mutual information (MI) and maximal leakage (MaxL), respectively. For all other \alpha this measure is shown to be the Arimoto channel capacity. Several properties of this measure are proven including: (i) quasi-convexity in the mapping between the original and disclosed datasets; (ii) data processing inequalities; and (iii) a composition property. A full version of this paper is in [1].",

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N2 - A tunable measure for information leakage called maximal a-leakage is introduced. This measure quantifies the maximal gain of an adversary in refining a tilted version of its prior belief of any (potentially random) function of a dataset conditioning on a disclosed dataset. The choice of \alpha determines the specific adversarial action ranging from refining a belief for \alpha=1 to guessing the best posterior for \alpha=\infty, and for these extremal values this measure simplifies to mutual information (MI) and maximal leakage (MaxL), respectively. For all other \alpha this measure is shown to be the Arimoto channel capacity. Several properties of this measure are proven including: (i) quasi-convexity in the mapping between the original and disclosed datasets; (ii) data processing inequalities; and (iii) a composition property. A full version of this paper is in [1].

AB - A tunable measure for information leakage called maximal a-leakage is introduced. This measure quantifies the maximal gain of an adversary in refining a tilted version of its prior belief of any (potentially random) function of a dataset conditioning on a disclosed dataset. The choice of \alpha determines the specific adversarial action ranging from refining a belief for \alpha=1 to guessing the best posterior for \alpha=\infty, and for these extremal values this measure simplifies to mutual information (MI) and maximal leakage (MaxL), respectively. For all other \alpha this measure is shown to be the Arimoto channel capacity. Several properties of this measure are proven including: (i) quasi-convexity in the mapping between the original and disclosed datasets; (ii) data processing inequalities; and (iii) a composition property. A full version of this paper is in [1].

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A Tunable Measure for Information Leakage

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