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Differential Privacy on Trust Graphs

Authors Badih Ghazi , Ravi Kumar , Pasin Manurangsi , Serena Wang

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  • Security and privacy → Information-theoretic techniques
  • Security and privacy → Trust frameworks
  • Theory of computation → Computational complexity and cryptography
  • Theory of computation → Theory of database privacy and security
Keywords
  • Differential privacy
  • trust graphs
  • minimum dominating set
  • packing number

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Abstract

We study differential privacy (DP) in a multi-party setting where each party only trusts a (known) subset of the other parties with its data. Specifically, given a trust graph where vertices correspond to parties and neighbors are mutually trusting, we give a DP algorithm for aggregation with a much better privacy-utility trade-off than in the well-studied local model of DP (where each party trusts no other party). We further study a robust variant where each party trusts all but an unknown subset of at most t of its neighbors (where t is a given parameter), and give an algorithm for this setting. We complement our algorithms with lower bounds, and discuss implications of our work to other tasks in private learning and analytics.

Cite As Get BibTex

Badih Ghazi, Ravi Kumar, Pasin Manurangsi, and Serena Wang. Differential Privacy on Trust Graphs. In 16th Innovations in Theoretical Computer Science Conference (ITCS 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 325, pp. 53:1-53:23, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025) https://doi.org/10.4230/LIPIcs.ITCS.2025.53

Author Details

Badih Ghazi
  • Google Research, Mountain View, CA, USA
Ravi Kumar
  • Google Research, Mountain View, CA, USA
Pasin Manurangsi
  • Google Research, Bangkok, Thailand
Serena Wang
  • Google Research, Mountain View, CA, USA
  • Harvard University, Cambridge, MA, USA

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