论文标题
通过差异隐私在对抗性稳健的流算法上
Adversarially Robust Streaming Algorithms via Differential Privacy
论文作者
论文摘要
据说，即使在自适应对手恶意选择数据流的情况下保持数据流，也可以保持其准确性的保证，从而在对手方面具有稳定性。我们在流算法的对抗鲁棒性与差异隐私的概念之间建立了联系。该连接使我们能够设计新的对抗性鲁棒流算法，以优于许多有趣的参数制度的当前最新构造。
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.
