We consider the setup of a constrained optimization problem with two agents $E_1$ and $E_2$ who jointly wish to learn the optimal solution set while keeping their feasible sets $\mathcal{P}_1$ and $\mathcal{P}_2$ private from each other. The objective function $f$ is globally known and each feasible set is a collection of points from a global alphabet. We adopt a sequential symmetric private information retrieval (SPIR) framework where one of the agents (say $E_1$) privately checks in $\mathcal{P}_2$, the presence of candidate solutions of the problem constrained to $\mathcal{P}_1$ only, while learning no further information on $\mathcal{P}_2$ than the solution alone. Further, we extract an information theoretically private threshold PSI (ThPSI) protocol from our scheme and characterize its download cost. We show that, compared to privately acquiring the feasible set $\mathcal{P}_1\cap \mathcal{P}_2$ using an SPIR-based private set intersection (PSI) protocol, and finding the optimum, our scheme is better as it incurs less information leakage and less download cost than the former. Over all possible uniform mappings of $f$ to a fixed range of values, our scheme outperforms the former with a high probability.
Privacy preserving distributed profile matching in mobile social network
在该文献中，在称为\emph{mobile social network}的特定网络上下文中提出了隐私保护分布式简档匹配协议。这些网络通常部署在或多或少敌对的环境中，需要严格的安全机制。与此同时，由于这些异构网络通常由平板电脑或手机等无线组件构成，因此能源和计算资源有限。这就是为什么本文提出了一种新的加密算法，具有较高的安全性，同时保留资源。该方法是基于椭圆曲线密码学，更具体地说，在一个几乎完全同态密码系统的超奇异椭圆曲线，导致一个安全和有效的保护隐私的分布式配置文件匹配。
Secure Distributed/Federated Learning: Prediction-Privacy Trade-Off for Multi-Agent System
University of Southern California（南加州大学）
分散学习是一种有效的新兴范式，用于提高多个有界计算代理的计算能力。在大数据时代，在分布式和联邦学习（DL和FL）框架内执行推理，中央服务器需要处理大量数据，同时依赖各种代理执行多个分布式训练任务。考虑到分布式计算的拓扑结构，隐私已经成为一个首要的问题。此外，假设代理的信息处理能力有限，需要一个复杂的\textit{privacy-preserving decentralization}来确保有效的计算。为此，我们研究的\textit{privacy-aware server to multi-agent assignment}问题受到信息处理的限制与每个代理，同时保持隐私和确保学习信息的消息，通过分布式私人联邦学习（DPFL）的方法收到代理全球终端。为了找到一个分散的计划，两个代理系统，我们制定了一个优化问题，平衡隐私和准确性，考虑到与每个代理相关联的压缩约束的质量。我们提出了一个迭代收敛算法交替自洽方程。我们还对所提出的解决方案进行了数值评估，以显示隐私预测的权衡，并证明了新方法在确保DL和FL隐私方面的有效性。
On Privacy, Security, and Trustworthiness in Distributed Wireless Large AI Models (WLAM)
College of Information Science and Electronic Engineering, Zhejiang University（浙江大学信息科学与电子工程学院）；Key Laboratory of Information Processing, Communication and Networking of Zhejiang Province (IPCAN)（浙江省信息处理、通信与网络重点实验室）；National Mobile Communications Research Laboratory, Southeast University（东南大学国家移动通信重点实验室）；School of Information and Communication Engineering, Beijing University of Posts and Communications（北京邮电大学信息与通信工程学院）；Department of Electronic Engineering, Tsinghua University（清华大学电子工程系）；Center for 6G Technology, Khalifa University of Science and Technology（哈利法科技大学6G技术中心）
