License: Creative Commons Attribution 3.0 Unported license (CC BY 3.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.FORC.2020.10
URN: urn:nbn:de:0030-drops-120265
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Ligett, Katrina ; Peale, Charlotte ; Reingold, Omer

Bounded-Leakage Differential Privacy

LIPIcs-FORC-2020-10.pdf (0.5 MB)


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.

BibTeX - Entry

  author =	{Katrina Ligett and Charlotte Peale and Omer Reingold},
  title =	{{Bounded-Leakage Differential Privacy}},
  booktitle =	{1st Symposium on Foundations of Responsible Computing (FORC 2020)},
  pages =	{10:1--10:20},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-142-9},
  ISSN =	{1868-8969},
  year =	{2020},
  volume =	{156},
  editor =	{Aaron Roth},
  publisher =	{Schloss Dagstuhl--Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{},
  URN =		{urn:nbn:de:0030-drops-120265},
  doi =		{10.4230/LIPIcs.FORC.2020.10},
  annote =	{Keywords: differential privacy, applications, privacy, leakage, auxiliary information}

Keywords: differential privacy, applications, privacy, leakage, auxiliary information
Collection: 1st Symposium on Foundations of Responsible Computing (FORC 2020)
Issue Date: 2020
Date of publication: 18.05.2020

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