A Framework for Adversarially Robust Streaming Algorithms

Omri Ben-Eliezer, Rajesh Jayaram, David P. Woodruff, Eylon Yogev

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

We investigate the adversarial robustness of streaming algorithms. In this context, an algorithm is considered robust if its performance guarantees hold even if the stream is chosen adaptively by an adversary that observes the outputs of the algorithm along the stream and can react in an online manner. While deterministic streaming algorithms are inherently robust, many central problems in the streaming literature do not admit sublinear-space deterministic algorithms; on the other hand, classical space-efficient randomized algorithms for these problems are generally not adversarially robust. This raises the natural question of whether there exist efficient adversarially robust (randomized) streaming algorithms for these problems.

Original languageEnglish
Pages (from-to)6-13
Number of pages8
JournalSIGMOD Record
Volume50
Issue number1
DOIs
StatePublished - Mar 2021
Externally publishedYes

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