{"title":"SecFed:基于多密钥同态加密的安全高效联合学习","authors":"Yuxuan Cai, Wenxiu Ding, Yuxuan Xiao, Zheng Yan, Ximeng Liu, Zhiguo Wan","doi":"10.1109/TDSC.2023.3336977","DOIUrl":null,"url":null,"abstract":"Federated Learning (FL) is widely used in various industries because it effectively addresses the predicament of isolated data island. However, eavesdroppers is capable of inferring user privacy from the gradients or models transmitted in FL. Homomorphic Encryption (HE) can be applied in FL to protect sensitive data owing to its computability over ciphertexts. However, traditional HE as a single-key system cannot prevent dishonest users from intercepting and decrypting the ciphertexts from cooperative users in FL. Guaranteeing privacy and efficiency in this multi-user scenario is still a challenging target. In this article, we propose a secure and efficient Federated Learning scheme (SecFed) based on multi-key HE to preserve user privacy and delegate some operations to TEE to improve efficiency while ensuring security. Specifically, we design the first TEE-based multi-key HE cryptosystem (EMK-BFV) to support privacy-preserving FL and optimize operation efficiency. Furthermore, we provide an offline protection mechanism to ensure the normal operation of system with disconnected participants. Finally, we give their security proofs and show their efficiency and superiority through comprehensive simulations and comparisons with existing schemes. SecFed offers a 3x performance improvement over TEE-based scheme and a 2x performance improvement over HE-based solution.","PeriodicalId":13047,"journal":{"name":"IEEE Transactions on Dependable and Secure Computing","volume":null,"pages":null},"PeriodicalIF":7.0000,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"SecFed: A Secure and Efficient Federated Learning Based on Multi-Key Homomorphic Encryption\",\"authors\":\"Yuxuan Cai, Wenxiu Ding, Yuxuan Xiao, Zheng Yan, Ximeng Liu, Zhiguo Wan\",\"doi\":\"10.1109/TDSC.2023.3336977\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Federated Learning (FL) is widely used in various industries because it effectively addresses the predicament of isolated data island. However, eavesdroppers is capable of inferring user privacy from the gradients or models transmitted in FL. Homomorphic Encryption (HE) can be applied in FL to protect sensitive data owing to its computability over ciphertexts. However, traditional HE as a single-key system cannot prevent dishonest users from intercepting and decrypting the ciphertexts from cooperative users in FL. Guaranteeing privacy and efficiency in this multi-user scenario is still a challenging target. In this article, we propose a secure and efficient Federated Learning scheme (SecFed) based on multi-key HE to preserve user privacy and delegate some operations to TEE to improve efficiency while ensuring security. Specifically, we design the first TEE-based multi-key HE cryptosystem (EMK-BFV) to support privacy-preserving FL and optimize operation efficiency. Furthermore, we provide an offline protection mechanism to ensure the normal operation of system with disconnected participants. Finally, we give their security proofs and show their efficiency and superiority through comprehensive simulations and comparisons with existing schemes. SecFed offers a 3x performance improvement over TEE-based scheme and a 2x performance improvement over HE-based solution.\",\"PeriodicalId\":13047,\"journal\":{\"name\":\"IEEE Transactions on Dependable and Secure Computing\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":7.0000,\"publicationDate\":\"2024-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Dependable and Secure Computing\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.1109/TDSC.2023.3336977\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, HARDWARE & ARCHITECTURE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Dependable and Secure Computing","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1109/TDSC.2023.3336977","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, HARDWARE & ARCHITECTURE","Score":null,"Total":0}
引用次数: 2
摘要
联合学习(FL)能有效解决数据孤岛的困境,因此被广泛应用于各行各业。然而,窃听者有能力从 FL 中传输的梯度或模型中推断出用户隐私。同态加密(Homorphic Encryption,HE)由于其对密码文本的可计算性,可以应用于 FL 来保护敏感数据。然而,传统的单密钥系统同态加密无法防止不诚实用户截获和解密 FL 中合作用户的密文。在这种多用户场景中如何保证隐私和效率仍然是一个具有挑战性的目标。在本文中,我们提出了一种基于多密钥 HE 的安全高效的联盟学习方案(SecFed),以保护用户隐私,并将一些操作委托给 TEE,从而在确保安全的同时提高效率。具体来说,我们设计了首个基于 TEE 的多密钥 HE 密码系统(EMK-BFV),以支持隐私保护 FL 并优化操作效率。此外,我们还提供了一种离线保护机制,以确保系统在参与者断开连接的情况下正常运行。最后,我们给出了它们的安全证明,并通过全面的模拟和与现有方案的比较,展示了它们的效率和优越性。SecFed 的性能比基于 TEE 的方案提高了 3 倍,比基于 HE 的方案提高了 2 倍。
SecFed: A Secure and Efficient Federated Learning Based on Multi-Key Homomorphic Encryption
Federated Learning (FL) is widely used in various industries because it effectively addresses the predicament of isolated data island. However, eavesdroppers is capable of inferring user privacy from the gradients or models transmitted in FL. Homomorphic Encryption (HE) can be applied in FL to protect sensitive data owing to its computability over ciphertexts. However, traditional HE as a single-key system cannot prevent dishonest users from intercepting and decrypting the ciphertexts from cooperative users in FL. Guaranteeing privacy and efficiency in this multi-user scenario is still a challenging target. In this article, we propose a secure and efficient Federated Learning scheme (SecFed) based on multi-key HE to preserve user privacy and delegate some operations to TEE to improve efficiency while ensuring security. Specifically, we design the first TEE-based multi-key HE cryptosystem (EMK-BFV) to support privacy-preserving FL and optimize operation efficiency. Furthermore, we provide an offline protection mechanism to ensure the normal operation of system with disconnected participants. Finally, we give their security proofs and show their efficiency and superiority through comprehensive simulations and comparisons with existing schemes. SecFed offers a 3x performance improvement over TEE-based scheme and a 2x performance improvement over HE-based solution.
期刊介绍:
The "IEEE Transactions on Dependable and Secure Computing (TDSC)" is a prestigious journal that publishes high-quality, peer-reviewed research in the field of computer science, specifically targeting the development of dependable and secure computing systems and networks. This journal is dedicated to exploring the fundamental principles, methodologies, and mechanisms that enable the design, modeling, and evaluation of systems that meet the required levels of reliability, security, and performance.
The scope of TDSC includes research on measurement, modeling, and simulation techniques that contribute to the understanding and improvement of system performance under various constraints. It also covers the foundations necessary for the joint evaluation, verification, and design of systems that balance performance, security, and dependability.
By publishing archival research results, TDSC aims to provide a valuable resource for researchers, engineers, and practitioners working in the areas of cybersecurity, fault tolerance, and system reliability. The journal's focus on cutting-edge research ensures that it remains at the forefront of advancements in the field, promoting the development of technologies that are critical for the functioning of modern, complex systems.