Combining Federated Learning (FL) with a Trusted Execution Environment (TEE) is a promising approach for realizing privacy-preserving FL, which has garnered significant academic attention in recent years. Implementing the TEE on the server side enables each round of FL to proceed without exposing the client's gradient information to untrusted servers. This addresses usability gaps in existing secure aggregation schemes as well as utility gaps in differentially private FL. However, to address the issue using a TEE, the vulnerabilities of server-side TEEs need to be considered -- this has not been sufficiently investigated in the context of FL. The main technical contribution of this study is the analysis of the vulnerabilities of TEE in FL and the defense. First, we theoretically analyze the leakage of memory access patterns, revealing the risk of sparsified gradients, which are commonly used in FL to enhance communication efficiency and model accuracy. Second, we devise an inference attack to link memory access patterns to sensitive information in the training dataset. Finally, we propose an oblivious yet efficient aggregation algorithm to prevent memory access pattern leakage. Our experiments on real-world data demonstrate that the proposed method functions efficiently in practical scales.

翻译：将联邦学习与可信执行环境相结合是近年来备受学术界关注的保护隐私联邦学习方案。通过在服务端部署可信执行环境，可使每轮联邦学习在不向不可信服务器暴露客户端梯度信息的情况下推进，从而弥补现有安全聚合方案在可用性以及差分隐私联邦学习在效用性方面的不足。然而，采用可信执行环境时需要考虑服务端TEE的脆弱性——这一问题在联邦学习场景中尚未得到充分研究。本研究的主要技术贡献在于分析联邦学习中TEE的脆弱性并提出防御措施。首先，我们从理论上分析内存访问模式的泄露问题，揭示稀疏化梯度（联邦学习中常用于提升通信效率与模型精度的技术）存在的风险；其次，我们设计了一种推理攻击方法，将内存访问模式与训练数据集的敏感信息相关联；最后，我们提出一种具备遗忘性且高效的聚合算法，以防止内存访问模式泄露。基于真实数据的实验表明，所提方法在实用规模下仍能保持高效运行。