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

Department of Computer Science

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

42 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 - 1 Jan 2020
Event	34th Conference on Neural Information Processing Systems, NeurIPS 2020 - Virtual, Online Duration: 6 Dec 2020 → 12 Dec 2020

ASJC Scopus subject areas

Computer Networks and Communications
Information Systems
Signal Processing

Cite this

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title = "Adversarially robust streaming algorithms via differential privacy",

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.",

author = "Avinatan Hassidim and Haim Kaplan and Yishay Mansour and Yossi Matias and Uri Stemmer",

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AU - Hassidim, Avinatan

AU - Kaplan, Haim

AU - Mansour, Yishay

AU - Matias, Yossi

AU - Stemmer, Uri

PY - 2020/1/1

Y1 - 2020/1/1

N2 - 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.

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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M3 - Conference article

AN - SCOPUS:85101611257

SN - 1049-5258

VL - 2020-December

JO - Advances in Neural Information Processing Systems

JF - Advances in Neural Information Processing Systems

T2 - 34th Conference on Neural Information Processing Systems, NeurIPS 2020

Y2 - 6 December 2020 through 12 December 2020

ER -

Adversarially robust streaming algorithms via differential privacy

Abstract

ASJC Scopus subject areas

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