Differentially private graphical degree sequences and synthetic graphs

Research output: Chapter in Book/Report/Conference proceedingConference contribution

19 Citations (Scopus)

Abstract

We present an algorithm for releasing graphical degree sequences of simple undirected graphs under the framework of differential privacy. The algorithm is designed to provide utility for statistical inference in random graph models whose sufficient statistics are functions of degree sequences. Specifically, we focus on the tasks of existence of maximum likelihood estimates, parameter estimation and goodness-of-fit testing for the beta model of random graphs. We show the usefulness of our algorithm by evaluating it empirically on simulated and real-life datasets. As the released degree sequence is graphical, our algorithm can also be used to release synthetic graphs under the beta model.

Original languageEnglish (US)
Title of host publicationPrivacy in Statistical Databases - UNESCO Chair in Data Privacy, International Conference, PSD 2012, Proceedings
Pages273-285
Number of pages13
Volume7556 LNCS
DOIs
StatePublished - 2012
EventInternational Conference on Privacy in Statistical Databases, PSD 2012 - Palermo, Italy
Duration: Sep 26 2012Sep 28 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7556 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

OtherInternational Conference on Privacy in Statistical Databases, PSD 2012
CountryItaly
CityPalermo
Period9/26/129/28/12

Fingerprint

Degree Sequence
Graph in graph theory
Random Graphs
Sufficient Statistics
Graph Model
Goodness of fit
Maximum Likelihood Estimate
Statistical Inference
Simple Graph
Undirected Graph
Parameter estimation
Maximum likelihood
Privacy
Parameter Estimation
Statistics
Testing
Graphics
Model

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Karwa, V., & Slavkovic, A. B. (2012). Differentially private graphical degree sequences and synthetic graphs. In Privacy in Statistical Databases - UNESCO Chair in Data Privacy, International Conference, PSD 2012, Proceedings (Vol. 7556 LNCS, pp. 273-285). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7556 LNCS). https://doi.org/10.1007/978-3-642-33627-0-21
Karwa, Vishesh ; Slavkovic, Aleksandra B. / Differentially private graphical degree sequences and synthetic graphs. Privacy in Statistical Databases - UNESCO Chair in Data Privacy, International Conference, PSD 2012, Proceedings. Vol. 7556 LNCS 2012. pp. 273-285 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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Karwa, V & Slavkovic, AB 2012, Differentially private graphical degree sequences and synthetic graphs. in Privacy in Statistical Databases - UNESCO Chair in Data Privacy, International Conference, PSD 2012, Proceedings. vol. 7556 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7556 LNCS, pp. 273-285, International Conference on Privacy in Statistical Databases, PSD 2012, Palermo, Italy, 9/26/12. https://doi.org/10.1007/978-3-642-33627-0-21

Differentially private graphical degree sequences and synthetic graphs. / Karwa, Vishesh; Slavkovic, Aleksandra B.

Privacy in Statistical Databases - UNESCO Chair in Data Privacy, International Conference, PSD 2012, Proceedings. Vol. 7556 LNCS 2012. p. 273-285 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7556 LNCS).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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AB - We present an algorithm for releasing graphical degree sequences of simple undirected graphs under the framework of differential privacy. The algorithm is designed to provide utility for statistical inference in random graph models whose sufficient statistics are functions of degree sequences. Specifically, we focus on the tasks of existence of maximum likelihood estimates, parameter estimation and goodness-of-fit testing for the beta model of random graphs. We show the usefulness of our algorithm by evaluating it empirically on simulated and real-life datasets. As the released degree sequence is graphical, our algorithm can also be used to release synthetic graphs under the beta model.

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Karwa V, Slavkovic AB. Differentially private graphical degree sequences and synthetic graphs. In Privacy in Statistical Databases - UNESCO Chair in Data Privacy, International Conference, PSD 2012, Proceedings. Vol. 7556 LNCS. 2012. p. 273-285. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-642-33627-0-21