Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/162985
Title: Efficient dropout-resilient aggregation for privacy-preserving machine learning
Authors: Liu, Ziyao
Guo, Jiale
Lam, Kwok-Yan
Zhao, Jun
Keywords: Engineering::Computer science and engineering
Issue Date: 2022
Source: Liu, Z., Guo, J., Lam, K. & Zhao, J. (2022). Efficient dropout-resilient aggregation for privacy-preserving machine learning. IEEE Transactions On Information Forensics and Security, 14(8), 3163592-. https://dx.doi.org/10.1109/TIFS.2022.3163592
Journal: IEEE Transactions on Information Forensics and Security 
Abstract: Machine learning (ML) has been widely recognized as an enabler of the global trend of digital transformation. With the increasing adoption of data-hungry machine learning algorithms, personal data privacy has emerged as one of the key concerns that could hinder the success of digital transformation. As such, Privacy-Preserving Machine Learning (PPML) has received much attention of the machine learning community, from academic researchers to industry practitioners to government regulators. However, organizations are faced with the dilemma that, on the one hand, they are encouraged to share data to enhance ML performance, but on the other hand, they could potentially be breaching the relevant data privacy regulations. Practical PPML typically allows multiple participants to individually train their ML models, which are then aggregated to construct a global model in a privacy-preserving manner, e.g., based on multi-party computation or homomorphic encryption. Nevertheless, in most important applications of large-scale PPML, e.g., by aggregating clients’ gradients to update a global model for federated learning, such as consumer behavior modeling of mobile application services, some participants are inevitably resource-constrained mobile devices, which may drop out of the PPML system due to their mobility nature [1]. Therefore, the resilience of privacy-preserving aggregation has become an important problem to be tackled because of its real-world application potential and impacts. In this paper, we propose a scalable privacy-preserving aggregation scheme that can tolerate dropout by participants at any time, and is secure against both semi-honest and active malicious adversaries by setting proper system parameters. By replacing communication-intensive building blocks with a seed homomorphic pseudo-random generator, and relying on the additive homomorphic property of Shamir secret sharing scheme, our scheme outperforms state-of-the-art schemes by up to 6.37× in runtime and provides a stronger dropout-resilience. The simplicity of our scheme makes it attractive both for implementation and for further improvements.
URI: https://hdl.handle.net/10356/162985
ISSN: 1556-6013
DOI: 10.1109/TIFS.2022.3163592
Schools: School of Computer Science and Engineering 
Rights: © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/TIFS.2022.3163592.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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