Adversarially Robust Streaming Algorithms via Differential Privacy
Avinatan Hasidim, Haim Kaplan, Yishay Mansour, Yossi Matias, Uri Stemmer
Oral presentation: Orals & Spotlights Track 10: Social/Privacy
on 2020-12-08T06:00:00-08:00 - 2020-12-08T06:15:00-08:00
on 2020-12-08T06:00:00-08:00 - 2020-12-08T06:15:00-08:00
Poster Session 2 (more posters)
on 2020-12-08T09:00:00-08:00 - 2020-12-08T11:00:00-08:00
GatherTown: Algorithms and learning theory ( Town B2 - Spot C3 )
on 2020-12-08T09:00:00-08:00 - 2020-12-08T11:00:00-08:00
GatherTown: Algorithms and learning theory ( Town B2 - Spot C3 )
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Toggle Abstract Paper (in Proceedings / .pdf)
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.