Michael Oluwole, Ruiyao Liu, Aleksander Stevens, Chenxi Qiu · 2026 · arXiv
DOI: 10.48550/arXiv.2610.27615 · open-access PDF
メトリック差別プライバシー(mDP)の線形プログラミング(LP)メカニズムは、細かい粒子でタスク意識のあるユーティリティコントロールを可能にしますが、計算的に高価で、分散設定ではプライバシー脆弱になります:ユーザ特有のLPコフェクションマトリックス(例えば、距離および損失ブロック)を中央ソーラーにアップロードすると、自社で機密情報を漏らすことができます。
Machine translation (Meta M2M100); the English original follows.
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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