一种保护隐私的分布式资源分配优化方法
A Privacy-preserving Method to Optimize Distributed Resource Allocation
摘要：
我们考虑一个资源分配问题，涉及大量的代理人与个人的隐私约束，和一个中央运营商，其目标是优化全球，可能是非凸的，成本，同时满足代理人的约束，例如能源运营商负责管理的能源消费灵活性的许多个人消费者。我们提供了一个隐私保护算法，计算资源的最优分配，避免每个代理透露她的私人信息（约束和个人解决方案）既不中央运营商，也不向第三方。我们的方法依赖于一个聚合过程：我们迭代计算的全球资源分配，并逐步确保存在的解聚，即个人档案满足代理的私人约束，通过一个协议，涉及到生成多面体切割和安全多方计算（SMC）。为了获得这些削减，我们使用一种替代投影方法，该方法由每个代理在本地实现，以保护她的隐私需求。我们特别强调的情况下，当地和全球的限制，定义一个运输多面体。然后，我们提供了理论上的收敛性估计与数值结果，表明该算法可以有效地用于解决分配问题在高维，同时解决隐私问题。
英文摘要：
We consider a resource allocation problem involving a large number of agents with individual constraints subject to privacy, and a central operator whose objective is to optimize a global, possibly nonconvex, cost while satisfying the agents' constraints, for instance an energy operator in charge of the management of energy consumption flexibilities of many individual consumers. We provide a privacy-preserving algorithm that does compute the optimal allocation of resources, avoiding each agent to reveal her private information (constraints and individual solution profile) neither to the central operator nor to a third party. Our method relies on an aggregation procedure: we compute iteratively a global allocation of resources, and gradually ensure existence of a disaggregation, that is individual profiles satisfying agents' private constraints, by a protocol involving the generation of polyhedral cuts and secure multiparty computations (SMC). To obtain these cuts, we use an alternate projection method, which is implemented locally by each agent, preserving her privacy needs. We adress especially the case in which the local and global constraints define a transportation polytope. Then, we provide theoretical convergence estimates together with numerical results, showing that the algorithm can be effectively used to solve the allocation problem in high dimension, while addressing privacy issues.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
