Differential privacy for diffusion LMS algorithm over a distributed network

This article presents a way to improve the Diffusion LMS algorithm in practical scenarios while maintaining data privacy. It focuses on reducing external errors and ensuring accurate estimation of the parameter vector through distributed and adaptive privacy protection. By combining these improvemen...

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Bibliographic Details
Published inDigital signal processing Vol. 153; p. 104634
Main Authors Zandi, Sajad, Korki, Mehdi
Format Journal Article
LanguageEnglish
Published Elsevier Inc 01.10.2024
Subjects
Differential privacy
Distributed estimation
Impulsive noise
Mean square deviation
Differential privacy
Mean square deviation
Impulsive noise
Distributed estimation
Online AccessGet full text
ISSN1051-2004
1095-4333
DOI10.1016/j.dsp.2024.104634

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Summary:This article presents a way to improve the Diffusion LMS algorithm in practical scenarios while maintaining data privacy. It focuses on reducing external errors and ensuring accurate estimation of the parameter vector through distributed and adaptive privacy protection. By combining these improvements, the overall performance of the algorithm can be significantly enhanced without compromising Mean Square Deviation (MSD). One important aspect is tuning the privacy noise power based on data sensitivity. This work provides a novel solution for improving the performance and privacy of the Diffusion LMS algorithm with implications for future research. The mean square error and evolution behavior of the algorithms are also analyzed, showing their effectiveness and robustness in preserving privacy and MSD over the network.
ISSN:1051-2004
1095-4333
DOI:10.1016/j.dsp.2024.104634