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Classical streaming algorithms operate under the (not always reasonable)assumption that the input stream is fixed in advance. Recently, there is agrowing interest in designing robust streaming algorithms that provide provableguarantees even when the input stream is chosen adaptively as the executionprogresses. We propose a new framework for robust streaming that combinestechniques from two recently suggested frameworks by Hassidim et al. [NeurIPS2020] and by Woodruff and Zhou [FOCS 2021]. These recently suggested frameworksrely on very different ideas, each with its own strengths and weaknesses. Wecombine these two frameworks into a single hybrid framework that obtains the``best of both worlds'', thereby solving a question left open by Woodruff andZhou.

A Framework for Adversarial Streaming via Differential Privacy and Difference Estimators