A Framework for Adversarial Streaming via Differential Privacy and Difference Estimators

TL;DR

This paper introduces a hybrid framework for robust streaming algorithms that combines differential privacy and difference estimators, enhancing adaptability and provable guarantees in dynamic input scenarios.

Contribution

It merges two recent frameworks into a unified approach, addressing open questions and improving robustness in adaptive streaming environments.

Findings

Successfully combines differential privacy with difference estimators.

Provides provable guarantees for adaptive input streams.

Solves an open problem from prior research.

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.