Linear-programming (LP) mechanisms for metric differential privacy (mDP) enable fine-grained, task-aware utility control, but they are computationally expensive and become privacy-fragile in distributed settings: uploading user-specific LP coefficient matrices (e.g., distance and loss blocks) to a central solver can itself leak sensitive information. We present DISTA-LP, a privacy-preserving distributed mDP optimization framework that replaces raw coefficient uploads with compact surrogate parameterizations and releases only calibrated, perturbed parameter vectors, thereby reducing both upload-stage leakage and problem dimensionality. On the server side, DISTA-LP reconstructs approximate coefficients and solves a scalable mastersubproblem formulation via Benders decomposition. To certify end-to-end privacy, we introduce a probabilistic mDP (PmDP) auditor that empirically quantifies the privacy loss induced by parameter release, allocates the required privacy budget prior to optimization, and composes it with the downstream mechanism to obtain an end-to-end mDP guarantee. Experiments on large realworld road-network datasets (Rome, New York City, and London) show that DISTA-LP achieves near-zero mDP violation rates, substantially reduces computation time compared to monolithic and hybrid baselines, and matches or improves utility, thereby demonstrating stronger upload privacy and significantly improved scalability.

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