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Private Counting of Distinct and k-Occurring Items in Time Windows

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

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Badih Ghazi
  • Google, Mountain View, CA, USA
Ravi Kumar
  • Google, Mountain View, CA, USA
Jelani Nelson
  • UC Berkeley, CA, USA
  • Google, Mountain View, CA, USA
Pasin Manurangsi
  • Google, Mountain View, CA, USA

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Badih Ghazi, Ravi Kumar, Jelani Nelson, and Pasin Manurangsi. Private Counting of Distinct and k-Occurring Items in Time Windows. In 14th Innovations in Theoretical Computer Science Conference (ITCS 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 251, pp. 55:1-55:24, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023) https://doi.org/10.4230/LIPIcs.ITCS.2023.55

Abstract

In this work, we study the task of estimating the numbers of distinct and k-occurring items in a time window under the constraint of differential privacy (DP). We consider several variants depending on whether the queries are on general time windows (between times t₁ and t₂), or are restricted to being cumulative (between times 1 and t₂), and depending on whether the DP neighboring relation is event-level or the more stringent item-level. We obtain nearly tight upper and lower bounds on the errors of DP algorithms for these problems. En route, we obtain an event-level DP algorithm for estimating, at each time step, the number of distinct items seen over the last W updates with error polylogarithmic in W; this answers an open question of Bolot et al. (ICDT 2013).

Subject Classification

ACM Subject Classification
  • Theory of computation → Theory of database privacy and security
Keywords
  • Differential Privacy
  • Algorithms
  • Distinct Elements
  • Time Windows

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