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Bounded-Leakage Differential Privacy

Authors Katrina Ligett, Charlotte Peale, Omer Reingold

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Author Details

Katrina Ligett
  • Computer Science Department, Hebrew University of Jerusalem, Israel
Charlotte Peale
  • Stanford University, Stanford, CA, USA
Omer Reingold
  • Computer Science Department, Stanford University, Stanford, CA, USA

Funding

  • Ligett, Katrina: This work was supported in part by Israel Science Foundation (ISF) grant #1044/16, United States Air Force and DARPA under contracts FA8750-16-C-0022 and FA8750-19-2-0222, and the Federmann Cyber Security Center in conjunction with the Israel national cyber directorate. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the United States Air Force and DARPA.
  • Reingold, Omer: Supported in part by NSF Award IIS-1908774 and by VMWare fellowship.

Acknowledgements

Part of this work was done while the first and third authors were visiting the Simons Institute for the Theory of Computing.

Cite AsGet BibTex

Katrina Ligett, Charlotte Peale, and Omer Reingold. Bounded-Leakage Differential Privacy. In 1st Symposium on Foundations of Responsible Computing (FORC 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 156, pp. 10:1-10:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)
https://doi.org/10.4230/LIPIcs.FORC.2020.10

Abstract

We introduce and study a relaxation of differential privacy [Dwork et al., 2006] that accounts for mechanisms that leak some additional, bounded information about the database. We apply this notion to reason about two distinct settings where the notion of differential privacy is of limited use. First, we consider cases, such as in the 2020 US Census [Abowd, 2018], in which some information about the database is released exactly or with small noise. Second, we consider the accumulation of privacy harms for an individual across studies that may not even include the data of this individual. The tools that we develop for bounded-leakage differential privacy allow us reason about privacy loss in these settings, and to show that individuals preserve some meaningful protections.

Subject Classification

ACM Subject Classification
  • Theory of computation → Theory of database privacy and security
Keywords
  • differential privacy
  • applications
  • privacy
  • leakage
  • auxiliary information

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References

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