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On Differentially Private Counting on Trees

Authors Badih Ghazi , Pritish Kamath , Ravi Kumar , Pasin Manurangsi , Kewen Wu

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Author Details

Badih Ghazi
  • Google, Mountain View, CA, US
Pritish Kamath
  • Google, Mountain View, CA, US
Ravi Kumar
  • Google, Mountain View, CA, US
Pasin Manurangsi
  • Google, Bangkok, Thailand
Kewen Wu
  • University of California at Berkeley, CA, US

Funding

  • Wu, Kewen: Most of this work was done while at Google.

Acknowledgements

KW wants to thank Xin Lyu for helpful references on the sparse vector technique. We thank anonymous ITCS'23 and ICALP'23 reviewers for helpful feedback.

Cite AsGet BibTex

Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, and Kewen Wu. On Differentially Private Counting on Trees. In 50th International Colloquium on Automata, Languages, and Programming (ICALP 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 261, pp. 66:1-66:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
https://doi.org/10.4230/LIPIcs.ICALP.2023.66

Abstract

We study the problem of performing counting queries at different levels in hierarchical structures while preserving individuals' privacy. Motivated by applications, we propose a new error measure for this problem by considering a combination of multiplicative and additive approximation to the query results. We examine known mechanisms in differential privacy (DP) and prove their optimality, under this measure, in the pure-DP setting. In the approximate-DP setting, we design new algorithms achieving significant improvements over known ones.

Subject Classification

ACM Subject Classification
  • Theory of computation → Theory of database privacy and security
Keywords
  • Differential Privacy
  • Algorithms
  • Trees
  • Hierarchies

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