一种隐私保护的柔性能源非侵入式管理解集算法
A Privacy-preserving Disaggregation Algorithm for Non-intrusive Management of Flexible Energy
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，其目标是优化的全球，可能是非凸的，成本，同时满足代理人的约束。我们专注于微电网运营商管理能源消耗灵活性的实际案例。本文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（约束和个人的解决方案）既不向中央运营商，也不向第三方。我们的方法依赖于一个聚合过程：我们保持一个全球性的资源分配，并逐步分解这种分配，以执行私人约束的满意度，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，我们使用了一种替代的预测方法，该方法由每个代理在本地实现，以保护她的隐私需求。我们的理论和数值结果表明，该方法的规模以及代理的数量变得很大，因此可以用来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 optimizing a global, possibly non-convex, cost while satisfying the agents'c onstraints. We focus on the practical case of the management of energy consumption flexibilities by the operator of a microgrid. This paper provides 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 maintain a global allocation of resources, and gradually disaggregate this allocation to enforce the satisfaction of private contraints, by a protocol involving the generation of polyhedral cuts and secure multiparty computations (SMC). To obtain these cuts, we use an alternate projections method à la Von Neumann, which is implemented locally by each agent, preserving her privacy needs. Our theoretical and numerical results show that the method scales well as the number of agents gets large, and thus can be used to solve the allocation problem in high dimension, while addressing privacy issues.
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