  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
{\epsilon}-differentially private.And we also show the trade-off between
convergence accuracy and privacy level. Finally, a numerical example is
provided for verification.
