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Algorithms with More Granular Differential Privacy Guarantees

Authors Badih Ghazi, Ravi Kumar, Pasin Manurangsi, Thomas Steinke

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Author Details

Badih Ghazi
  • Google, Mountain View, CA, USA
Ravi Kumar
  • Google, Mountain View, CA, USA
Pasin Manurangsi
  • Google, Mountain View, CA, USA
Thomas Steinke
  • Google, Mountain View, CA, USA

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Badih Ghazi, Ravi Kumar, Pasin Manurangsi, and Thomas Steinke. Algorithms with More Granular Differential Privacy Guarantees. In 14th Innovations in Theoretical Computer Science Conference (ITCS 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 251, pp. 54:1-54:24, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
https://doi.org/10.4230/LIPIcs.ITCS.2023.54

Abstract

Differential privacy is often applied with a privacy parameter that is larger than the theory suggests is ideal; various informal justifications for tolerating large privacy parameters have been proposed. In this work, we consider partial differential privacy (DP), which allows quantifying the privacy guarantee on a per-attribute basis. We study several basic data analysis and learning tasks in this framework, and design algorithms whose per-attribute privacy parameter is smaller that the best possible privacy parameter for the entire record of a person (i.e., all the attributes).

Subject Classification

ACM Subject Classification
  • Theory of computation → Theory of database privacy and security
Keywords
  • Differential Privacy
  • Algorithms
  • Per-Attribute Privacy

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