论文标题
袖珍诊断：安全联邦学习免受云中的中毒攻击
Pocket Diagnosis: Secure Federated Learning against Poisoning Attack in the Cloud
论文作者
论文摘要
由于在培训多个卫生机构（即数据岛（DIS））中培训联合模型的有效性，联合学习在医学诊断中变得普遍。但是，越来越大的Di级中毒攻击揭示了联邦学习中的脆弱性，该脆弱性将中毒的数据注入某些DIS，以破坏联邦模型的可用性。以前关于联合学习的工作在确保DIS的隐私和最终联合模型的可用性方面不足。在本文中，我们设计了一种安全的联合学习机制，该机制具有多个密钥，以防止Di级中毒攻击用于医学诊断，称为SFPA。具体而言，SFPA通过使用Multi-Key Secure Computitation提供了基于森林的联合学习的随机随机学习，从而保证了与DI相关信息的机密性。同时，提出了针对本地加密模型的安全防御策略，以防御Di-Level中毒攻击。最后，我们在公共云平台上进行的正式安全分析和经验测试证明了SFPA的安全性和效率以及其抵抗Di-Level中毒攻击的能力。
Federated learning has become prevalent in medical diagnosis due to its effectiveness in training a federated model among multiple health institutions (i.e. Data Islands (DIs)). However, increasingly massive DI-level poisoning attacks have shed light on a vulnerability in federated learning, which inject poisoned data into certain DIs to corrupt the availability of the federated model. Previous works on federated learning have been inadequate in ensuring the privacy of DIs and the availability of the final federated model. In this paper, we design a secure federated learning mechanism with multiple keys to prevent DI-level poisoning attacks for medical diagnosis, called SFPA. Concretely, SFPA provides privacy-preserving random forest-based federated learning by using the multi-key secure computation, which guarantees the confidentiality of DI-related information. Meanwhile, a secure defense strategy over encrypted locally-submitted models is proposed to defense DI-level poisoning attacks. Finally, our formal security analysis and empirical tests on a public cloud platform demonstrate the security and efficiency of SFPA as well as its capability of resisting DI-level poisoning attacks.
