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A Framework for Adversarial Streaming via Differential Privacy and Difference Estimators

Authors Idan Attias, Edith Cohen, Moshe Shechner, Uri Stemmer

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Author Details

Idan Attias
  • Ben-Gurion University, Beer Sheva, Israel
Edith Cohen
  • Google Research, Mountain View, CA, USA
  • Tel Aviv University, Israel
Moshe Shechner
  • Tel Aviv University, Israel
Uri Stemmer
  • Tel Aviv University, Israel
  • Google Research, Herzliya, Israel

Funding

  • Attias, Idan: Partially supported by the Vatat Scholarship from the Israeli Council for Higher Education, Kreitman School of Advanced Graduate Studies, and the Lynn and William Frankel Center for Computer Science at Ben-Gurion University.
  • Cohen, Edith: Partially supported by the Israel Science Foundation (grant 1595/19).
  • Shechner, Moshe: Partially supported by the Israel Science Foundation (grant 1871/19).
  • Stemmer, Uri: Partially supported by the Israel Science Foundation (grant 1871/19) and by Len Blavatnik and the Blavatnik Family foundation.

Cite AsGet BibTex

Idan Attias, Edith Cohen, Moshe Shechner, and Uri Stemmer. A Framework for Adversarial Streaming via Differential Privacy and Difference Estimators. In 14th Innovations in Theoretical Computer Science Conference (ITCS 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 251, pp. 8:1-8:19, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
https://doi.org/10.4230/LIPIcs.ITCS.2023.8

Abstract

Classical streaming algorithms operate under the (not always reasonable) assumption that the input stream is fixed in advance. Recently, there is a growing interest in designing robust streaming algorithms that provide provable guarantees even when the input stream is chosen adaptively as the execution progresses. We propose a new framework for robust streaming that combines techniques from two recently suggested frameworks by Hassidim et al. [NeurIPS 2020] and by Woodruff and Zhou [FOCS 2021]. These recently suggested frameworks rely on very different ideas, each with its own strengths and weaknesses. We combine these two frameworks into a single hybrid framework that obtains the "best of both worlds", thereby solving a question left open by Woodruff and Zhou.

Subject Classification

ACM Subject Classification
  • Theory of computation → Streaming, sublinear and near linear time algorithms
Keywords
  • Streaming
  • adversarial robustness
  • differential privacy

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References

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