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.
@InProceedings{attias_et_al:LIPIcs.ITCS.2023.8, author = {Attias, Idan and Cohen, Edith and Shechner, Moshe and Stemmer, Uri}, title = {{A Framework for Adversarial Streaming via Differential Privacy and Difference Estimators}}, booktitle = {14th Innovations in Theoretical Computer Science Conference (ITCS 2023)}, pages = {8:1--8:19}, series = {Leibniz International Proceedings in Informatics (LIPIcs)}, ISBN = {978-3-95977-263-1}, ISSN = {1868-8969}, year = {2023}, volume = {251}, editor = {Tauman Kalai, Yael}, publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik}, address = {Dagstuhl, Germany}, URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ITCS.2023.8}, URN = {urn:nbn:de:0030-drops-175115}, doi = {10.4230/LIPIcs.ITCS.2023.8}, annote = {Keywords: Streaming, adversarial robustness, differential privacy} }
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