论文标题
基于差异的私有ADMM分布式分布式离散最佳运输用于资源分配
Differentially Private ADMM-Based Distributed Discrete Optimal Transport for Resource Allocation
论文作者
论文摘要
最佳运输（OT）是一个框架，可以指导多个来源和目标网络中有效的资源分配策略的设计。为了简化大规模运输设计的计算复杂性，我们首先基于乘数的交替方向方法（ADMM）开发了分布式算法。但是，当攻击者拦截分布式ADMM更新过程中节点之间传达的传输决策时，这种分布式算法很容易受到敏感信息泄漏的影响。为此，我们通过向每个节点的决策添加适当的随机性，然后在每个Updice实例上与其他相应的节点共享，以基于输出变量扰动提出一个基于输出变量扰动的分布式机制。我们表明，开发的方案是私人的，这阻止了对手推断节点的机密信息，甚至知道运输决策。最后，我们通过案例研究证实了所设计算法的有效性。
Optimal transport (OT) is a framework that can guide the design of efficient resource allocation strategies in a network of multiple sources and targets. To ease the computational complexity of large-scale transport design, we first develop a distributed algorithm based on the alternating direction method of multipliers (ADMM). However, such a distributed algorithm is vulnerable to sensitive information leakage when an attacker intercepts the transport decisions communicated between nodes during the distributed ADMM updates. To this end, we propose a privacy-preserving distributed mechanism based on output variable perturbation by adding appropriate randomness to each node's decision before it is shared with other corresponding nodes at each update instance. We show that the developed scheme is differentially private, which prevents the adversary from inferring the node's confidential information even knowing the transport decisions. Finally, we corroborate the effectiveness of the devised algorithm through case studies.
