SASH: Efficient Secure Aggregation Based on SHPRG For Federated Learning

2021/11/24 by Zizhen Liu, Si Chen, Liu, Zizhen +9 · 3 citations
Computer Science · #Cryptography and Data Security #Cryptography and Security (cs.CR) #Distributed #FOS: Computer and information sciences #Parallel #Privacy-Preserving Technologies in Data #Stochastic Gradient Optimization Techniques #and Cluster Computing (cs.DC)

paper · pdf · doi:10.48550/arxiv.2111.12321

openalex publication_date 2021/11/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

To prevent private training data leakage in Fed?erated Learning systems, we propose a novel se?cure aggregation scheme based on seed homomor?phic pseudo-random generator (SHPRG), named SASH. SASH leverages the homomorphic property of SHPRG to simplify the masking and demask?ing scheme, which for each of the clients and for the server, entails an overhead linear w.r.t model size and constant w.r.t number of clients. We prove that even against worst-case colluding adversaries, SASH preserves training data privacy, while being resilient to dropouts without extra overhead. We experimentally demonstrate SASH significantly improves the efficiency to 20 times over baseline, especially in the more realistic case where the numbers of clients and model size become large, and a cer?tain percentage of clients drop out from the system.

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