信息安全研究2024，Vol.10Issue(9)：804-810,7.DOI:10.12379/j.issn.2096-1057.2024.09.03
物联网感知环境中抗投毒可验证安全联邦学习方案
A Poisoning-resistant Verifiable Secure Federated Learning Scheme in IoT Perception Environments
摘要
Abstract
To address the issue of model poisoning during predictive model training in the IoT intelligent sensing phase,this study proposes an anti-poisoning attack scheme with verification capabilities.The scheme employs a cosine similarity clustering mechanism and a filtering strategy as a trusted third-party detection algorithm,integrating homomorphic encryption for authentication.Additionally,lightweight data encryption is used to protect the privacy of local model data.The Shamir Secret Sharing algorithm ensures robustness in model training against users dropout.By introducing a trusted third party,the scheme effectively detects and prevents dishonest users or attackers from compromising the accuracy of federated learning models.Simulation results demonstrate that the scheme can accurately detect model data involved in training while ensuring the security of users'local model data and handling large volumes of heterogeneous data in IoT intelligent sensing environments.关键词
联邦学习/投毒攻击/物联网智能感知/隐私保护/同态加密Key words
federated learning/poisoning attack/IoT intelligent perception/privacy protection/homomorphic encryption分类
信息技术与安全科学引用本文复制引用
韩刚,马炜燃,张应辉,刘伟,盛丽玲..物联网感知环境中抗投毒可验证安全联邦学习方案[J].信息安全研究,2024,10(9):804-810,7.基金项目
国家自然科学基金项目(62102312) （62102312）
陕西省重点研发计划项目(2024GX-YBXM-079) （2024GX-YBXM-079）
ISN全国重点实验室开放课题(ISN24-13) （ISN24-13）
陕西省高校青年创新团队项目(23JP160) （23JP160）
