Robust Streaming Against Low-Memory Adversaries
Omri Ben-Eliezer, Krzysztof Onak, Sandeep Silwal

TL;DR
This paper explores robust streaming algorithms that can withstand adaptive adversaries with limited memory, achieving improved performance in settings where the adversary's memory is constrained.
Contribution
It introduces the first models and algorithms for robust streaming against low-memory adaptive adversaries, expanding the understanding of adversarial models in streaming.
Findings
Efficient algorithms are possible against memoryless adversaries for certain problems.
Memoryless adversaries can generate complex streams, challenging existing robustification techniques.
New approaches similar to the computation paths framework are effective against low-memory adversaries.
Abstract
Robust streaming, the study of streaming algorithms that provably work when the stream is generated by an adaptive adversary, has seen tremendous progress in recent years. However, fundamental barriers remain: the best known algorithm for turnstile -estimation in the robust streaming setting is exponentially worse than in the oblivious setting, and closing this gap seems difficult. Arguably, one possible cause of this barrier is the adversarial model, which may be too strong: unlike the space-bounded streaming algorithm, the adversary can memorize the entire history of the interaction with the algorithm. Can we then close the exponential gap if we insist that the adversary itself is an adaptive but low-memory entity, roughly as powerful as (or even weaker than) the algorithm? In this work we present the first set of models and results aimed towards this question. We design…
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