DISTA-LP: Privacy-Preserving Distributed Linear Programming for Metric Differential Privacy

Michael Oluwole, Ruiyao Liu, Aleksander Stevens, Chenxi Qiu · 2026 · arXiv

DOI: 10.48550/arXiv.2610.27615 · open-access PDF

Arabic

تتيح آليات الترجمة اللوجستية (LP) للحساسية التفاعلية التفاعلية (mDP) السيطرة على الأدوات اللوجستية ذات الصلة بالمهام، ولكنها تكلفة حسابية وتصبح ضعيفة للحساسية في الإعدادات الموزعة: تثبيت مارتنات معادل LP محددة للمستخدم (على سبيل المثال، قسما من المسافة والخسائر) إلى حلول المركزية يمكن أن تنزف المعلومات ذات الصلة بنفسها.

Machine translation (Meta M2M100); the English original follows.

Abstract

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

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