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Privacy-Preserving Distributed Processing: Metrics, Bounds and Algorithms
IEEE Transactions on Information Forensics and Security ( IF 6.8 ) Pub Date : 2021-01-08 , DOI: 10.1109/tifs.2021.3050064
Qiongxiu Li , Jaron Skovsted Gundersen , Richard Heusdens , Mads Grasboll Christensen

Privacy-preserving distributed processing has recently attracted considerable attention. It aims to design solutions for conducting signal processing tasks over networks in a decentralized fashion without violating privacy. Many existing algorithms can be adopted to solve this problem such as differential privacy, secure multiparty computation, and the recently proposed distributed optimization based subspace perturbation algorithms. However, since each of them is derived from a different context and has different metrics and assumptions, it is hard to choose or design an appropriate algorithm in the context of distributed processing. In order to address this problem, we first propose general mutual information based information-theoretical metrics that are able to compare and relate these existing algorithms in terms of two key aspects: output utility and individual privacy. We consider two widely-used adversary models, the passive and eavesdropping adversary. Moreover, we derive a lower bound on individual privacy which helps to understand the nature of the problem and provides insights on which algorithm is preferred given different conditions. To validate the above claims, we investigate a concrete example and compare a number of state-of-the-art approaches in terms of the concerned aspects using not only theoretical analysis but also numerical validation. Finally, we discuss and provide principles for designing appropriate algorithms for different applications.

中文翻译:

维护隐私的分布式处理:度量标准,界限和算法

保持隐私的分布式处理最近引起了相当大的关注。它旨在设计解决方案,以分散方式在网络上执行信号处理任务,而又不侵犯隐私。可以采用许多现有算法来解决此问题,例如差分隐私,安全的多方计算以及最近提出的基于分布式优化的子空间摄动算法。但是,由于它们每个都是从不同的上下文派生的,并且具有不同的度量和假设,因此很难在分布式处理的上下文中选择或设计合适的算法。为了解决这个问题,我们首先提出基于通用互信息的信息理论指标,该指标能够在两个关键方面比较和关联这些现有算法:输出实用程序和个人隐私。我们考虑了两个广泛使用的对手模型,即被动和窃听对手。此外,我们得出了个人隐私的下限,这有助于了解问题的本质,并提供在不同条件下首选算法的见解。为了验证上述权利要求,我们调查了一个具体示例,并不仅使用理论分析而且还使用数值验证,就相关方面比较了许多最新方法。最后,我们讨论并提供了为不同应用程序设计适当算法的原理。我们得出个人隐私的下限,这有助于了解问题的性质,并提供在不同条件下首选哪种算法的见解。为了验证上述权利要求,我们调查了一个具体示例,并不仅使用理论分析而且还使用数值验证,就相关方面比较了许多最新方法。最后,我们讨论并提供了为不同应用程序设计适当算法的原理。我们得出个人隐私的下限,这有助于了解问题的性质,并提供在不同条件下首选哪种算法的见解。为了验证上述权利要求,我们调查了一个具体示例,并不仅使用理论分析而且还使用数值验证,就相关方面比较了许多最新方法。最后,我们讨论并提供了为不同应用程序设计适当算法的原理。
更新日期:2021-02-05
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