License: Creative Commons Attribution 4.0 International license (CC BY 4.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.ITCS.2023.8
URN: urn:nbn:de:0030-drops-175115
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2023/17511/
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Attias, Idan ; Cohen, Edith ; Shechner, Moshe ; Stemmer, Uri

A Framework for Adversarial Streaming via Differential Privacy and Difference Estimators

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LIPIcs-ITCS-2023-8.pdf (0.8 MB)

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.

BibTeX - Entry

@InProceedings{attias_et_al:LIPIcs.ITCS.2023.8,
  author =	{Attias, Idan and Cohen, Edith and Shechner, Moshe and Stemmer, Uri},
  title =	{{A Framework for Adversarial Streaming via Differential Privacy and Difference Estimators}},
  booktitle =	{14th Innovations in Theoretical Computer Science Conference (ITCS 2023)},
  pages =	{8:1--8:19},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-263-1},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{251},
  editor =	{Tauman Kalai, Yael},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/opus/volltexte/2023/17511},
  URN =		{urn:nbn:de:0030-drops-175115},
  doi =		{10.4230/LIPIcs.ITCS.2023.8},
  annote =	{Keywords: Streaming, adversarial robustness, differential privacy}
}

Keywords: Streaming, adversarial robustness, differential privacy
Collection: 14th Innovations in Theoretical Computer Science Conference (ITCS 2023)
Issue Date: 2023
Date of publication: 01.02.2023

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