论文标题
安全汇总并不是您所需要的：在联合学习中减轻噪音耐受性的隐私攻击
Secure Aggregation Is Not All You Need: Mitigating Privacy Attacks with Noise Tolerance in Federated Learning
论文作者
论文摘要
联合学习是一种协作方法，旨在在创建AI模型时保留数据隐私。当前的联邦学习方法倾向于在很大程度上依赖安全的聚合协议来保护数据隐私。但是，在某种程度上，这样的协议假定协调联合学习过程的实体（即服务器）并非完全恶意或不诚实。我们调查如果服务器完全恶意并尝试获取私人敏感数据的访问权，则可以确保可能会产生的聚合漏洞。此外，我们提供了一种进一步防御这种恶意服务器的方法，并证明了在联合学习环境中重建数据的已知攻击的有效性。
Federated learning is a collaborative method that aims to preserve data privacy while creating AI models. Current approaches to federated learning tend to rely heavily on secure aggregation protocols to preserve data privacy. However, to some degree, such protocols assume that the entity orchestrating the federated learning process (i.e., the server) is not fully malicious or dishonest. We investigate vulnerabilities to secure aggregation that could arise if the server is fully malicious and attempts to obtain access to private, potentially sensitive data. Furthermore, we provide a method to further defend against such a malicious server, and demonstrate effectiveness against known attacks that reconstruct data in a federated learning setting.
