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Data Publishing against Realistic Adversaries

Publication ,  Journal Article
Machanavajjhala, A; Gehrke, J; Götz, M
Published in: Proceedings of the VLDB Endowment
January 1, 2009

Privacy in data publishing has received much attention recently. The key to defining privacy is to model knowledge of the attacker - if the attacker is assumed to know too little, the published data can be easily attacked, if the attacker is assumed to know too much, the published data has little utility. Previous work considered either quite ignorant adversaries or nearly omniscient adversaries. In this paper, we introduce a new class of adversaries that we call realistic adversaries who live in the unexplored space in between. Realistic adversaries have knowledge from external sources with an associated stubbornness indicating the strength of their knowledge. We then introduce a novel privacy framework called epsilon-privacy that allows us to guard against realistic adversaries. We also show that prior privacy definitions are instantiations of our framework. In a thorough experimental study with real census data we show that e-privacy allows us to publish data with high utility while defending against strong adversaries. © 2009 VLDB Endowment.

Duke Scholars

Published In

Proceedings of the VLDB Endowment

DOI

EISSN

2150-8097

Publication Date

January 1, 2009

Volume

2

Issue

1

Start / End Page

790 / 801

Related Subject Headings

  • 4605 Data management and data science
  • 0807 Library and Information Studies
  • 0806 Information Systems
  • 0802 Computation Theory and Mathematics
 

Citation

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MLA
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Machanavajjhala, A., Gehrke, J., & Götz, M. (2009). Data Publishing against Realistic Adversaries. Proceedings of the VLDB Endowment, 2(1), 790–801. https://doi.org/10.14778/1687627.1687717
Machanavajjhala, A., J. Gehrke, and M. Götz. “Data Publishing against Realistic Adversaries.” Proceedings of the VLDB Endowment 2, no. 1 (January 1, 2009): 790–801. https://doi.org/10.14778/1687627.1687717.
Machanavajjhala A, Gehrke J, Götz M. Data Publishing against Realistic Adversaries. Proceedings of the VLDB Endowment. 2009 Jan 1;2(1):790–801.
Machanavajjhala, A., et al. “Data Publishing against Realistic Adversaries.” Proceedings of the VLDB Endowment, vol. 2, no. 1, Jan. 2009, pp. 790–801. Scopus, doi:10.14778/1687627.1687717.
Machanavajjhala A, Gehrke J, Götz M. Data Publishing against Realistic Adversaries. Proceedings of the VLDB Endowment. 2009 Jan 1;2(1):790–801.

Published In

Proceedings of the VLDB Endowment

DOI

EISSN

2150-8097

Publication Date

January 1, 2009

Volume

2

Issue

1

Start / End Page

790 / 801

Related Subject Headings

  • 4605 Data management and data science
  • 0807 Library and Information Studies
  • 0806 Information Systems
  • 0802 Computation Theory and Mathematics