Adaptive Differentially Quantized Subspace Perturbation (ADQSP)

Adaptive Differentially Quantized Subspace Perturbation (ADQSP): A Unified Framework for Privacy-Preserving Distributed Average Consensus

Li, Qiongxiu (Tsinghua University)
Gundersen, Jaron Skovsted (Aalborg University)
Lopuhaa-Zwakenberg, Milan (University of Twente)
Heusdens, R. (TU Delft Signal Processing Systems; Netherlands Defence Academy)

2024

Privacy-preserving distributed average consensus has received significant attention recently due to its wide applicability. Based on the achieved performances, existing approaches can be broadly classified into perfect accuracy-prioritized approaches such as secure multiparty computation (SMPC), and worst-case privacy-prioritized approaches such as differential privacy (DP). Methods of the first class achieve perfect output accuracy but reveal some private information, while methods from the second class provide privacy against the strongest adversary at the cost of a loss of accuracy. In this paper, we propose a general approach named adaptive differentially quantized subspace perturbation (ADQSP) which combines quantization schemes with so-called subspace perturbation. Although not relying on cryptographic primitives, the proposed approach enjoys the benefits of both accuracy-prioritized and privacy-prioritized methods and is able to unify them. More specifically, we show that by varying a single quantization parameter the proposed method can vary between SMPC-type performances and DP-type performances. Our results show the potential of exploiting traditional distributed signal processing tools for providing cryptographic guarantees. In addition to a comprehensive theoretical analysis, numerical validations are conducted to substantiate our results.

consensus
data aggregation
decentralized networks
differential privacy
quantization
Secure multiparty computation
subspace perturbation

http://resolver.tudelft.nl/uuid:5398d36a-a785-4f27-991d-aaffb23be2e8

https://doi.org/10.1109/TIFS.2023.3343599

2024-07-22

1556-6013

IEEE Transactions on Information Forensics and Security, 19, 1780-1793

Institutional Repository

journal article

© 2024 Qiongxiu Li, Jaron Skovsted Gundersen, Milan Lopuhaa-Zwakenberg, R. Heusdens