A Concentration of Measure Approach to Database De-anonymization

Farhad Shirani; Siddharth Garg; Elza Erkip

doi:10.1109/ISIT.2019.8849392

A Concentration of Measure Approach to Database De-anonymization

Farhad Shirani, Siddharth Garg, Elza Erkip

Electrical and Computer Engineering

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

Abstract

In this paper, matching of correlated high-dimensional databases is investigated. A stochastic database model is considered where the correlation among the database entries is governed by an arbitrary joint distribution. Concentration of measure theorems such as typicality and laws of large numbers are used to develop a database matching scheme and derive necessary conditions for successful matching. Furthermore, it is shown that these conditions are tight through a converse result which characterizes a set of distributions on the database entries for which reliable matching is not possible. The necessary and sufficient conditions for reliable matching are evaluated in the cases when the database entries are independent and identically distributed as well as under Markovian database models.

Original language	English (US)
Title of host publication	2019 IEEE International Symposium on Information Theory, ISIT 2019 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2748-2752
Number of pages	5
ISBN (Electronic)	9781538692912
DOIs	https://doi.org/10.1109/ISIT.2019.8849392
State	Published - Jul 2019
Event	2019 IEEE International Symposium on Information Theory, ISIT 2019 - Paris, France Duration: Jul 7 2019 → Jul 12 2019

Publication series

Name	IEEE International Symposium on Information Theory - Proceedings
Volume	2019-July
ISSN (Print)	2157-8095

Conference

Conference	2019 IEEE International Symposium on Information Theory, ISIT 2019
Country/Territory	France
City	Paris
Period	7/7/19 → 7/12/19

ASJC Scopus subject areas

Theoretical Computer Science
Information Systems
Modeling and Simulation
Applied Mathematics

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10.1109/ISIT.2019.8849392

Cite this

Shirani, F., Garg, S., & Erkip, E. (2019). A Concentration of Measure Approach to Database De-anonymization. In 2019 IEEE International Symposium on Information Theory, ISIT 2019 - Proceedings (pp. 2748-2752). Article 8849392 (IEEE International Symposium on Information Theory - Proceedings; Vol. 2019-July). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISIT.2019.8849392

A Concentration of Measure Approach to Database De-anonymization. / Shirani, Farhad; Garg, Siddharth ; Erkip, Elza.
2019 IEEE International Symposium on Information Theory, ISIT 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. p. 2748-2752 8849392 (IEEE International Symposium on Information Theory - Proceedings; Vol. 2019-July).

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

Shirani, F, Garg, S & Erkip, E 2019, A Concentration of Measure Approach to Database De-anonymization. in 2019 IEEE International Symposium on Information Theory, ISIT 2019 - Proceedings., 8849392, IEEE International Symposium on Information Theory - Proceedings, vol. 2019-July, Institute of Electrical and Electronics Engineers Inc., pp. 2748-2752, 2019 IEEE International Symposium on Information Theory, ISIT 2019, Paris, France, 7/7/19. https://doi.org/10.1109/ISIT.2019.8849392

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A Concentration of Measure Approach to Database De-anonymization

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