@inproceedings{a5cc005e30624d5a808a0320b3e3b994,
title = "A Framework for Adversarial Streaming via Differential Privacy and Difference Estimators",
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.",
keywords = "Streaming, adversarial robustness, differential privacy",
author = "Idan Attias and Edith Cohen and Moshe Shechner and Uri Stemmer",
note = "Publisher Copyright: {\textcopyright} Idan Attias, Edith Cohen, Moshe Shechner, and Uri Stemmer; licensed under Creative Commons License CC-BY 4.0.; 14th Innovations in Theoretical Computer Science Conference, ITCS 2023 ; Conference date: 10-01-2023 Through 13-01-2023",
year = "2023",
month = jan,
day = "1",
doi = "10.4230/LIPIcs.ITCS.2023.8",
language = "English",
series = "Leibniz International Proceedings in Informatics, LIPIcs",
publisher = "Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing",
editor = "Kalai, \{Yael Tauman\}",
booktitle = "14th Innovations in Theoretical Computer Science Conference, ITCS 2023",
address = "Germany",
}