论文标题
通过加密和结构化的功能扰动，私人和准确的分散化优化
Private and Accurate Decentralized Optimization via Encrypted and Structured Functional Perturbation
论文作者
论文摘要
我们提出了一种分散的优化算法，该算法在不牺牲精度的情况下保留了代理的成本功能的隐私，称为EFPSN。该算法采用Paillier加密系统来构建零和功能扰动。然后，基于扰动的成本函数，可以利用任何现有的分散优化算法来获得准确的解决方案。从理论上讲，我们证明了efpsn是（Epsilon，delta） - 不同的是私人，并且可以在故意的参数设置下获得几乎完美的隐私。数值实验进一步证实了算法的有效性。
We propose a decentralized optimization algorithm that preserves the privacy of agents' cost functions without sacrificing accuracy, termed EFPSN. The algorithm adopts Paillier cryptosystem to construct zero-sum functional perturbations. Then, based on the perturbed cost functions, any existing decentralized optimization algorithm can be utilized to obtain the accurate solution. We theoretically prove that EFPSN is (epsilon, delta)-differentially private and can achieve nearly perfect privacy under deliberate parameter settings. Numerical experiments further confirm the effectiveness of the algorithm.
