本文则提出了一个新的隐私保护联邦学习框架 PEFL(Privacy-Enhanced Federated Learning) 作为桥梁来解决上述的矛盾问题。其中使用同态加密 (HE) 来解决前者,也就是抵御推理攻击,使用Median 技术来解决后者,也就是抗投毒攻击。值得一提的是本文是第一个在密文上检测异常数据的 FL 框架。实验表明 PEFL 能够有效的抵御...
As such, this paper proposes a privacy-enhanced FL scheme, based on cryptographic mechanisms that allow both the data significance evaluation and weighted aggregation of local models in a privacy-preserving manner. Experimental results show that our scheme is practical and secure....
成员证明的隐私保护联邦学习(译Privacy-preserving federated learning with membership proof) 联邦学习是一种分布式机器学习,由多个协作者通过受保护的梯度训练模型。为了实现对用户退出的鲁棒性,现有的实用隐私保护联合学习方案都是基于(t,N)门限秘密共享的。但这样的方案通常需要依靠一个强有力的假设来保证安全性:阈值...
Privacy-Enhanced Over-the-Air Federated Learning via Client-Driven Power Balancing Kim, Bumjun,Seo, Hyowoon,Choi, Wan - 《Computer Networks》 - 2024 - 被引量: 0 A Security-Enhanced Federated Learning Scheme Based on Homomorphic Encryption and Secret Sharing Lingling Zhang - 《Mathematics》 - 20...
While security is at the core of FL, there are still many articles referred to distributed machine learning with no security guarantee as “federated learning”, which are not satisfied with the FL definition supposed to be. To this end, in this paper, we reiterate the concept of federated ...
Vertical Federated Learning (VFL) has many applications in the field of smart healthcare with excellent performance. However, current VFL systems usually primarily focus on the privacy protection during model training, while the preparation of training data receives little attention. In real-world appli...
Keywords Artificial intelligence; Machine learning; Distributed learning; Federated learning; Federated machine learning; Security Privacy; Abstract 联邦学习(FL)还处于起步阶段并且在公众中还没有获得太多的信任,主要是因为它未知的安全性和隐私含义。本文旨在提供关于FL 的安全和隐私方面的全面研究,并对相关方法和各...
Machine LearningData TechnologyInsurTechRegTechAIMLFederated learning is a pioneering privacy-preserving data technology and also a new machine learning model trained on distributed data sets.Companies cdoi:10.2139/ssrn.3696609mietanka, MagorzataPithadia, Hirsh...
鉴于组会汇报,特地挑了篇论文来简单学习一下,本文原文A Privacy-Preserving Federated Learning for Multiparty Data Sharing,Yin et al. IEEE Trans. 2021. 随着5G和移动计算的快速发展,社交计算和社交物联网(IoT)领域的深度学习服务在过去几年丰富了我们的生活。具有计算能力的移动设备和物联网设备可以随时随地加入...
原文链接:Privacy Threats Analysis to Secure Federated Learning 涉及的知识点:隐私、联邦学习、人工智能、密码学与安全 摘要 提高隐私保护的方式:hiding messages transferred in encryption 隐藏信息 本文工作:分析了具有安全计算功能的工业级联邦学习框架中存在的隐私威胁,此类威胁广泛存在于线性回归、逻辑回归和决策树等...