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Private Counting of Distinct Elements in the Turnstile Model and Extensions

Authors Monika Henzinger , A. R. Sricharan , Teresa Anna Steiner

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Author Details

Monika Henzinger
  • Institute of Science and Technology, Klosterneuburg, Austria
A. R. Sricharan
  • Faculty of Computer Science, Doctoral School Computer Science, University of Vienna, Austria
Teresa Anna Steiner
  • Technical University of Denmark, Lyngby, Denmark

Funding

  • Henzinger, Monika: This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (MoDynStruct, No. 101019564) and the Austrian Science Fund (FWF) grant DOI 10.55776/Z422, grant DOI 10.55776/I5982, and grant DOI 10.55776/P33775 with additional funding from the netidee SCIENCE Stiftung, 2020–2024.
  • Steiner, Teresa Anna: Supported by a research grant (VIL51463) from VILLUM FONDEN.

Cite AsGet BibTex

Monika Henzinger, A. R. Sricharan, and Teresa Anna Steiner. Private Counting of Distinct Elements in the Turnstile Model and Extensions. In Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 317, pp. 40:1-40:21, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)
https://doi.org/10.4230/LIPIcs.APPROX/RANDOM.2024.40

Abstract

Privately counting distinct elements in a stream is a fundamental data analysis problem with many applications in machine learning. In the turnstile model, Jain et al. [NeurIPS2023] initiated the study of this problem parameterized by the maximum flippancy of any element, i.e., the number of times that the count of an element changes from 0 to above 0 or vice versa. They give an item-level (ε,δ)-differentially private algorithm whose additive error is tight with respect to that parameterization. In this work, we show that a very simple algorithm based on the sparse vector technique achieves a tight additive error for item-level (ε,δ)-differential privacy and item-level ε-differential privacy with regards to a different parameterization, namely the sum of all flippancies. Our second result is a bound which shows that for a large class of algorithms, including all existing differentially private algorithms for this problem, the lower bound from item-level differential privacy extends to event-level differential privacy. This partially answers an open question by Jain et al. [NeurIPS2023].

Subject Classification

ACM Subject Classification
  • Security and privacy
Keywords
  • differential privacy
  • turnstile model
  • counting distinct elements

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References

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