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Differentially Private Aggregation via Imperfect Shuffling

Authors Badih Ghazi, Ravi Kumar, Pasin Manurangsi, Jelani Nelson, Samson Zhou

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Author Details

Badih Ghazi
  • Google Research, Mountain View, CA, USA
Ravi Kumar
  • Google Research, Mountain View, CA, USA
Pasin Manurangsi
  • Google Research, Mountain View, CA, USA
Jelani Nelson
  • University of California at Berkeley, CA, USA
  • Google Research, Mountain View, CA, USA
Samson Zhou
  • University of California at Berkeley, CA, USA
  • Rice University, Houston, TX, USA

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Badih Ghazi, Ravi Kumar, Pasin Manurangsi, Jelani Nelson, and Samson Zhou. Differentially Private Aggregation via Imperfect Shuffling. In 4th Conference on Information-Theoretic Cryptography (ITC 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 267, pp. 17:1-17:22, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023) https://doi.org/10.4230/LIPIcs.ITC.2023.17

Abstract

In this paper, we introduce the imperfect shuffle differential privacy model, where messages sent from users are shuffled in an almost uniform manner before being observed by a curator for private aggregation. We then consider the private summation problem. We show that the standard split-and-mix protocol by Ishai et. al. [FOCS 2006] can be adapted to achieve near-optimal utility bounds in the imperfect shuffle model. Specifically, we show that surprisingly, there is no additional error overhead necessary in the imperfect shuffle model.

Subject Classification

ACM Subject Classification
  • Security and privacy → Human and societal aspects of security and privacy
Keywords
  • Differential privacy
  • private summation
  • shuffle model

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