Privacy-Preserving Distributed Processing: Metrics, Bounds, and Algorithms
| Autor: | Qiongxiu Li, Jaron Skovsted Gundersen, Mads Grasboll Christensen, Richard Heusdens |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Signal processing
Signal Processing (eess.SP) FOS: Computer and information sciences Distributed databases Computer Science - Cryptography and Security Computer Networks and Communications Computer science information-theoretic Distributed computing 0211 other engineering and technologies Context (language use) 02 engineering and technology Distributed processing subspace perturbation differential privacy FOS: Electrical engineering electronic engineering information engineering Differential privacy privacy-utility metric Electrical Engineering and Systems Science - Signal Processing Safety Risk Reliability and Quality Computer Science::Cryptography and Security 021110 strategic defence & security studies Measurement Distributed database Eavesdropping secure multiparty computation Adversary Mutual information consensus Secure multi-party computation Key (cryptography) Signal processing algorithms Algorithm Cryptography and Security (cs.CR) Adversary model |
| Zdroj: | Li, Q, Gundersen, J S, Heusdens, R & Christensen, M G 2021, ' Privacy-Preserving Distributed Processing: Metrics, Bounds and Algorithms ', I E E E Transactions on Information Forensics and Security, vol. 16, 9316966, pp. 2090-2103 . https://doi.org/10.1109/TIFS.2021.3050064 IEEE Transactions on Information Forensics and Security, 16 |
| ISSN: | 1556-6013 |
| DOI: | 10.48550/arxiv.2009.01098 |
| Popis: | 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 algorithms can be adopted to solve this problem such as differential privacy, secure multiparty computation, and the recently proposed distributed optimization based subspace perturbation. However, how these algorithms relate to each other is not fully explored yet. In this paper, we therefore first propose information-theoretic metrics based on mutual information. Using the proposed metrics, we are able to compare and relate a number of existing well-known algorithms. We then derive a lower bound on individual privacy that gives insights on the nature of the problem. To validate the above claims, we investigate a concrete example and compare a number of state-of-the-art approaches in terms of different aspects such as output utility, individual privacy and algorithm robustness against the number of corrupted parties, using not only theoretical analysis but also numerical validation. Finally, we discuss and provide principles for designing appropriate algorithms for different applications. Comment: 12 pages, 3 figures |
| Databáze: | OpenAIRE |
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