论文标题
私有分布式不匹配跟踪算法，用于约束耦合资源分配问题
Differentially Private Distributed Mismatch Tracking Algorithm for Constraint-Coupled Resource Allocation Problems
论文作者
论文摘要
本文考虑了无方向性网络上的隐私分布式约束耦合资源分配问题，每个代理都持有私人成本功能，并仅通过本地通信获得解决方案。出于隐私问题，我们掩盖了具有独立拉普拉斯噪声的交换信息，以潜在的攻击者潜在地访问所有网络通信。我们提出了一种差异性私有分布式不匹配跟踪算法（DIFF-DMAC），以实现资源的成本优势分配，同时保留隐私。采用恒定步骤，在Lipschitz梯度的标准假设和强凸度的标准假设下建立了均方根中DIFF-DMAC的线性收敛性。此外，从理论上讲，所提出的算法是ε-不同的私有。我们还显示了收敛准确性和隐私水平之间的权衡。最后，提供了一个数值示例供验证。
This paper considers privacy-concerned distributed constraint-coupled resource allocation problems over an undirected network, where each agent holds a private cost function and obtains the solution via only local communication. With privacy concerns, we mask the exchanged information with independent Laplace noise against a potential attacker with potential access to all network communications. We propose a differentially private distributed mismatch tracking algorithm (diff-DMAC) to achieve cost-optimal distribution of resources while preserving privacy. Adopting constant stepsizes, the linear convergence property of diff-DMAC in mean square is established under the standard assumptions of Lipschitz gradients and strong convexity. Moreover, it is theoretically proven that the proposed algorithm is ε-differentially private.And we also show the trade-off between convergence accuracy and privacy level. Finally, a numerical example is provided for verification.
