Abstract
A streaming algorithm is said to be adversarially robust if its accuracy guarantees are maintained even when the data stream is chosen maliciously, by an adaptive adversary. We establish a connection between adversarial robustness of streaming algorithms and the notion of differential privacy. This connection allows us to design new adversarially robust streaming algorithms that outperform the current state-of-The-Art constructions for many interesting regimes of parameters.
| Original language | English |
|---|---|
| Article number | 42 |
| Journal | Journal of the ACM |
| Volume | 69 |
| Issue number | 6 |
| DOIs | |
| State | Published - 24 Nov 2022 |
Bibliographical note
Publisher Copyright:© 2022 Copyright held by the owner/author(s).
Funding
Haim Kaplan was supported in part by the Israel Science Foundation (grant 1595/19) and by the Blavatnik family foundation. Yishay Mansour was supported in part by funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant 882396), by the Israel Science Foundation (grant 993/17), Tel Aviv University Center for AI and Data Science (TAD), and the Yandex Initiative for Machine Learning at Tel Aviv University. Uri Stemmer was supported in part by the Israel Science Foundation (grant 1871/19) and by the Blavatnik family foundation.
| Funders | Funder number |
|---|---|
| Yandex Initiative for Machine Learning | 1871/19 |
| Horizon 2020 Framework Programme | 882396, 993/17 |
| Blavatnik Family Foundation | |
| European Commission | |
| Israel Science Foundation | 1595/19 |
| Tel Aviv University |
Keywords
- Streaming
- differential privacy
- robustness
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