论文标题
一个基于密码学的新型隐私权分散化优化范式
A Novel Cryptography-Based Privacy-Preserving Decentralized Optimization Paradigm
论文作者
论文摘要
现有的大规模优化方案受到可扩展性和网络安全性的挑战。有了有利的可伸缩性，适应性和灵活性，在网络物理系统应用中广泛采用了分散和分布式优化范例。但是，大多数现有方法都在很大程度上依赖代理之间或代理商与系统操作员之间的明确信息交换，从而使整个框架容易面临隐私风险。为了解决这个问题，本文合成了密码学和分散的优化技术，以开发一种新型的隐私权分散优化范式。所提出的范式通常适用于具有不可分离的目标函数和线性耦合约束的强耦合凸优化问题。通过数值示例验证了所提出的范式的安全性和准确性。
Existing large-scale optimization schemes are challenged by both scalability and cyber-security. With the favorable scalability, adaptability, and flexibility, decentralized and distributed optimization paradigms are widely adopted in cyber-physical system applications. However, most existing approaches heavily rely on explicit information exchange between agents or between agents and the system operator, leading the entire framework prone to privacy risks. To tackle this issue, this paper synthesizes cryptography and decentralized optimization techniques to develop a novel privacy-preserving decentralized optimization paradigm. The proposed paradigm is generically applicable to strongly coupled convex optimization problems with nonseparable objective functions and linearly coupled constraints. The security and accuracy of the proposed paradigm are verified via numerical examples.
