Differentially Private ADMM-Based Distributed Discrete Optimal Transport for Resource Allocation
Differentially Private ADMM-Based Distributed Discrete Optimal Transport for Resource Allocation
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DOI：
10.1109/globecom48099.2022.10001511
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发表时间：
2022-11
期刊：
影响因子：
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通讯作者：
Jason Hughes;Juntao Chen
中科院分区：
文献类型：
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作者：
Jason Hughes;Juntao Chen
最优运输（OT）是一个框架，可以指导在多个源和目标的网络中设计有效的资源分配策略。为了降低大规模运输设计的计算复杂度，我们首先开发了一个分布式算法的交替方向乘法器（ADMM）的基础上。然而，这样的分布式算法是脆弱的敏感信息泄漏时，攻击者拦截的传输决定之间的节点之间的分布式ADMM更新。为此，我们提出了一种基于输出变量扰动的隐私保护分布式机制，通过在每个更新实例与其他相应节点共享之前，向每个节点的决策添加适当的随机性。我们表明，开发的计划是差分私人，这可以防止对手从推断节点的机密信息，即使知道的运输决策。最后，我们通过案例研究证实了所设计的算法的有效性。
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.