Adversarially Robust Streaming Algorithms via Differential Privacy
This addresses the challenge of robust data processing in streaming settings for applications like real-time analytics, though it appears incremental as it builds on existing concepts.
The paper tackled the problem of designing adversarially robust streaming algorithms that maintain accuracy under malicious data streams, and it established a connection to differential privacy to achieve new algorithms that outperform state-of-the-art methods in many parameter regimes.
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.