Adversarially robust streaming algorithms via differential privacy

Avinatan Hassidim; Haim Kaplan; Yishay Mansour; Yossi Matias; Uri Stemmer

Adversarially robust streaming algorithms via differential privacy

Avinatan Hassidim^*, Haim Kaplan, Yishay Mansour, Yossi Matias, Uri Stemmer

^*Corresponding author for this work

School of Computer Science

Research output: Contribution to journal › Conference article › peer-review

38 Scopus citations

Abstract

A streaming algorithm is said to be adversarially robust if its accuracy guarantees are maintained even when the data stream is chosen maliciously, by an adaptive adversary. We establish a connection between adversarial robustness of streaming algorithms and the notion of differential privacy. This connection allows us to design new adversarially robust streaming algorithms that outperform the current state-of-the-art constructions for many interesting regimes of parameters.

Original language	English
Journal	Advances in Neural Information Processing Systems
Volume	2020-December
State	Published - 2020
Event	34th Conference on Neural Information Processing Systems, NeurIPS 2020 - Virtual, Online Duration: 6 Dec 2020 → 12 Dec 2020

Funding

Funders	Funder number
Horizon 2020 Framework Programme	882396, 1871/19, 993/17
Blavatnik Family Foundation
European Research Council
German-Israeli Foundation for Scientific Research and Development	1367/2017
Israel Science Foundation	1595/19

Cite this

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abstract = "A streaming algorithm is said to be adversarially robust if its accuracy guarantees are maintained even when the data stream is chosen maliciously, by an adaptive adversary. We establish a connection between adversarial robustness of streaming algorithms and the notion of differential privacy. This connection allows us to design new adversarially robust streaming algorithms that outperform the current state-of-the-art constructions for many interesting regimes of parameters.",

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AU - Kaplan, Haim

AU - Mansour, Yishay

AU - Matias, Yossi

AU - Stemmer, Uri

PY - 2020

Y1 - 2020

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AB - A streaming algorithm is said to be adversarially robust if its accuracy guarantees are maintained even when the data stream is chosen maliciously, by an adaptive adversary. We establish a connection between adversarial robustness of streaming algorithms and the notion of differential privacy. This connection allows us to design new adversarially robust streaming algorithms that outperform the current state-of-the-art constructions for many interesting regimes of parameters.

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SN - 1049-5258

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Y2 - 6 December 2020 through 12 December 2020

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Adversarially robust streaming algorithms via differential privacy

Abstract

Funding

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