Privacy preservation for federated learning based on Gaussian noise scrambling

Cong Hu; Ting Lei; Shuang Wang; Zhen Yao; Peng Wang; Tingzeng Zhang

doi:10.1117/12.3005035

16 August 2023 Privacy preservation for federated learning based on Gaussian noise scrambling

Cong Hu, Ting Lei, Shuang Wang, Zhen Yao, Peng Wang, Tingzeng Zhang

Proceedings Volume 12787, Sixth International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2023); 127871E (2023) https://doi.org/10.1117/12.3005035
Event: 6th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE 2023), 2023, Shenyang, China

Abstract

This article discusses a privacy preservation for federated learning based on gaussian noise scrambling. The main objective of this model is to protect user data privacy and security by aggregating model parameters from multiple parties instead of raw data sets. However, attackers may still obtain sensitive information from the model parameter information transmitted during federated learning training through certain means. To address this issue, we propose a differential privacy noise addition scheme for federated learning. By adding noise to transmitted model parameters to some extent, it prevents attackers from inferring participant information through reverse inference. We also study Gaussian noise mechanism in differential privacy protection and prove it by combining characteristics of federated learning and blockchain technology for adding Gaussian noise size according to differential privacy requirements. This article tests their proposed differential privacy protection mechanism using the NSL-KDD dataset and finds that after adding differential privacy protection, models still have good anomaly recognition performance. This study contributes to the field of federated learning by proposing a novel approach for protecting user data privacy and security during training. The proposed approach can be used in various applications such as healthcare, finance, and social media where data privacy is crucial.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Cong Hu, Ting Lei, Shuang Wang, Zhen Yao, Peng Wang, and Tingzeng Zhang "Privacy preservation for federated learning based on Gaussian noise scrambling", Proc. SPIE 12787, Sixth International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2023), 127871E (16 August 2023); https://doi.org/10.1117/12.3005035

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KEYWORDS

Data modeling

Machine learning

Data privacy

Education and training

Performance modeling

Reverse modeling

Blockchain

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