Adversarially Robust Streaming Algorithms via Differential Privacy
Avinatan Hassidim, Haim Kaplan, Yishay Mansour, Yossi Matias, Uri Stemmer
2020年份
85被引次数
47顶会引用
摘要
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.
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引用它的顶会 Paper47
- Adaptive Machine UnlearningVarun Gupta, Christopher Jung, Seth Neel, Aaron Roth 等NeurIPS 2021 · 被引用 262 次
- The Price of Differential Privacy under Continual ObservationPalak Jain, Sofya Raskhodnikova, Satchit Sivakumar, Adam D. SmithICML 2023 · 被引用 63 次
- Adversarial Robustness of Streaming Algorithms through Importance SamplingVladimir Braverman, Avinatan Hassidim, Yossi Matias, Mariano Schain 等NeurIPS 2021 · 被引用 56 次
- On the Robustness of CountSketch to Adaptive InputsEdith Cohen, Xin Lyu, Jelani Nelson, Tamás Sarlós 等ICML 2022 · 被引用 29 次
- Adversarial laws of large numbers and optimal regret in online classificationNoga Alon, Omri Ben-Eliezer, Yuval Dagan, Shay Moran 等STOC 2021 · 被引用 23 次
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