论文标题
通过混合 - 马诺酮功能扰动保证分布式非凸优化的私密性
Guaranteed Privacy of Distributed Nonconvex Optimization via Mixed-Monotone Functional Perturbations
论文作者
论文摘要
在本文中，我们介绍了一种新的保证隐私概念，该概念要求相应的包含函数范围更改为真实函数的范围很小。特别是利用混合单酮包容函数，我们提出了一种隐私性的机制，用于非convex分布式优化，该机制基于确定性但未知的局部目标函数的仿射扰动，该函数比概率差异隐私更强。该设计需要一种强大的优化方法来表征通过最佳扰动可以实现的最佳准确性。随后，这用于指导保证的私有扰动机制的改进，该机制可以通过理论上界实现可量化的精度，该理论上限被证明与所选优化算法无关。
In this paper, we introduce a new notion of guaranteed privacy that requires that the change of the range of the corresponding inclusion function to the true function is small. In particular, leveraging mixed-monotone inclusion functions, we propose a privacy-preserving mechanism for nonconvex distributed optimization, which is based on deterministic, but unknown, affine perturbation of the local objective functions, which is stronger than probabilistic differential privacy. The design requires a robust optimization method to characterize the best accuracy that can be achieved by an optimal perturbation. Subsequently, this is used to guide the refinement of a guaranteed-private perturbation mechanism that can achieve a quantifiable accuracy via a theoretical upper bound that is shown to be independent of the chosen optimization algorithm.
