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

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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)


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
  • Streaming
  • adversarial robustness
  • differential privacy


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