基于差分隐私的对抗鲁棒流算法

IF 2.3 2区 计算机科学 Q2 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE Journal of the ACM Pub Date : 2022-11-24 DOI:https://dl.acm.org/doi/10.1145/3556972
Avinatan Hassidim, Haim Kaplan, Yishay Mansour, Yossi Matias, Uri Stemmer
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引用次数: 0

摘要

如果流算法在数据流被自适应对手恶意选择的情况下仍能保证其准确性,则称其具有对抗性鲁棒性。我们建立了流算法的对抗鲁棒性与差分隐私概念之间的联系。这种联系使我们能够设计新的对抗鲁棒的流算法,在许多有趣的参数制度下优于当前最先进的结构。
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Adversarially Robust Streaming Algorithms via Differential Privacy

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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来源期刊
Journal of the ACM
Journal of the ACM 工程技术-计算机:理论方法
CiteScore
7.50
自引率
0.00%
发文量
51
审稿时长
3 months
期刊介绍: The best indicator of the scope of the journal is provided by the areas covered by its Editorial Board. These areas change from time to time, as the field evolves. The following areas are currently covered by a member of the Editorial Board: Algorithms and Combinatorial Optimization; Algorithms and Data Structures; Algorithms, Combinatorial Optimization, and Games; Artificial Intelligence; Complexity Theory; Computational Biology; Computational Geometry; Computer Graphics and Computer Vision; Computer-Aided Verification; Cryptography and Security; Cyber-Physical, Embedded, and Real-Time Systems; Database Systems and Theory; Distributed Computing; Economics and Computation; Information Theory; Logic and Computation; Logic, Algorithms, and Complexity; Machine Learning and Computational Learning Theory; Networking; Parallel Computing and Architecture; Programming Languages; Quantum Computing; Randomized Algorithms and Probabilistic Analysis of Algorithms; Scientific Computing and High Performance Computing; Software Engineering; Web Algorithms and Data Mining
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