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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.
A streaming algorithm is adversarially robust if it is guaranteed to perform correctly even in the presence of an adaptive adversary. Recently, several sophisticated frameworks for robustification of classical streaming algorithms have been developed
Streaming algorithms are algorithms for processing large data streams, using only a limited amount of memory. Classical streaming algorithms operate under the assumption that the input stream is fixed in advance. Recently, there is a growing interest
We give the first single-pass streaming algorithm for Column Subset Selection with respect to the entrywise $ell_p$-norm with $1 leq p < 2$. We study the $ell_p$ norm loss since it is often considered more robust to noise than the standard Frobenius
Motivated by the recent discovery that the interpretation maps of CNNs could easily be manipulated by adversarial attacks against network interpretability, we study the problem of interpretation robustness from a new perspective of Renyi differential
We consider the problem of designing and analyzing differentially private algorithms that can be implemented on {em discrete} models of computation in {em strict} polynomial time, motivated by known attacks on floating point implementations of real-a