Portal de Periódicos da CAPES

Privacy‐preserving federated learning based on multi‐key homomorphic encryption

2022; Wiley; Volume: 37; Issue: 9 Linguagem: Inglês

10.1002/int.22818

1098-111X

Jing Ma, Si‐Ahmed Naas, Stephan Sigg, Xixiang Lyu,

Stochastic Gradient Optimization Techniques

International Journal of Intelligent SystemsVolume 37, Issue 9 p. 5880-5901 RESEARCH ARTICLE Privacy-preserving federated learning based on multi-key homomorphic encryption Jing Ma, Jing Ma orcid.org/0000-0002-5217-3910 School of Cyber Engineering, Xidian University, Xi'an, Shaanxi, ChinaSearch for more papers by this authorSi-Ahmed Naas, Si-Ahmed Naas orcid.org/0000-0002-4019-938X Department of Communications and Networking, Aalto University, Espoo, Uusimaa, FinlandSearch for more papers by this authorStephan Sigg, Stephan Sigg orcid.org/0000-0001-6118-3355 Department of Communications and Networking, Aalto University, Espoo, Uusimaa, FinlandSearch for more papers by this authorXixiang Lyu, Corresponding Author Xixiang Lyu [email protected] orcid.org/0000-0003-2879-241X School of Cyber Engineering, Xidian University, Xi'an, Shaanxi, China Correspondence Xixiang Lyu, School of Cyber Engineering, Xidian University, Xi'an, 710071 Shaanxi, China. Email: [email protected]Search for more papers by this author Jing Ma, Jing Ma orcid.org/0000-0002-5217-3910 School of Cyber Engineering, Xidian University, Xi'an, Shaanxi, ChinaSearch for more papers by this authorSi-Ahmed Naas, Si-Ahmed Naas orcid.org/0000-0002-4019-938X Department of Communications and Networking, Aalto University, Espoo, Uusimaa, FinlandSearch for more papers by this authorStephan Sigg, Stephan Sigg orcid.org/0000-0001-6118-3355 Department of Communications and Networking, Aalto University, Espoo, Uusimaa, FinlandSearch for more papers by this authorXixiang Lyu, Corresponding Author Xixiang Lyu [email protected] orcid.org/0000-0003-2879-241X School of Cyber Engineering, Xidian University, Xi'an, Shaanxi, China Correspondence Xixiang Lyu, School of Cyber Engineering, Xidian University, Xi'an, 710071 Shaanxi, China. Email: [email protected]Search for more papers by this author First published: 17 January 2022 https://doi.org/10.1002/int.22818Citations: 3Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Abstract With the advance of machine learning and the Internet of Things (IoT), security and privacy have become critical concerns in mobile services and networks. Transferring data to a central unit violates the privacy of sensitive data. Federated learning mitigates this need to transfer local data by sharing model updates only. However, privacy leakage remains an issue. This paper proposes xMK-CKKS, an improved version of the MK-CKKS multi-key homomorphic encryption protocol, to design a novel privacy-preserving federated learning scheme. In this scheme, model updates are encrypted via an aggregated public key before sharing with a server for aggregation. For decryption, a collaboration among all participating devices is required. Our scheme prevents privacy leakage from publicly shared model updates in federated learning and is resistant to collusion between k < N − 1 participating devices and the server. The evaluation demonstrates that the scheme outperforms other innovations in communication and computational cost while preserving model accuracy. Citing Literature Volume37, Issue9September 2022Pages 5880-5901 RelatedInformation