Corpus ID: 46845867

Privacy for All: Ensuring Fair and Equitable Privacy Protections

@inproceedings{Ekstrand2018PrivacyFA,
  title={Privacy for All: Ensuring Fair and Equitable Privacy Protections},
  author={Michael D. Ekstrand and R. Joshaghani and Hoda Mehrpouyan},
  booktitle={FAT},
  year={2018}
}
In this position paper, we argue for applying recent research on ensuring sociotechnical systems are fair and nondiscriminatory to the privacy protections those systems may provide. Privacy literature seldom considers whether a proposed privacy scheme protects all persons uniformly, irrespective of membership in protected classes or particular risk in the face of privacy failure. Just as algorithmic decision-making systems may have discriminatory outcomes even without explicit or deliberate… Expand

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References

SHOWING 1-10 OF 55 REFERENCES
Privacy and the Limits of Law
PHILOSOPHICAL THEORIES OF PRIVACY: IMPLICATIONS FOR AN ADEQUATE ONLINE PRIVACY POLICY
Privacy in Context - Technology, Policy, and the Integrity of Social Life
The So-Called Right to Privacy
Privacy as contextual integrity
Privacy protection, control of information, and privacy-enhancing technologies
How Much Is Enough? Choosing ε for Differential Privacy
k-Anonymity: A Model for Protecting Privacy
  • L. Sweeney
  • Computer Science
  • Int. J. Uncertain. Fuzziness Knowl. Based Syst.
  • 2002
Discrimination- and privacy-aware patterns
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