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cs.CR2026

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

Michael Oluwole, Ruiyao Liu, Aleksander Stevens +1

Linear-programming (LP) mechanisms for metric differential privacy (mDP) enable fine-grained, task-aware utility control, but they are computationally expensive and become privacy-…

cs.CR2026

Beyond Monolithic Perturbation: Heterogeneous Mechanism Design for Multi-Attribute Metric Differential Privacy

Ruiyao Liu, Michael Oluwole, Chenxi Qiu

Multi-attribute user records are inherently heterogeneous, often combining continuous, categorical, and binary attributes, and they frequently exhibit strong cross-attribute depend…

cs.CR2026

A Graph-Based Framework for Extending Metric Differential Privacy Mechanisms

Ruiyao Liu, Chenxi Qiu

Metric differential privacy (mDP) is well suited to structured secret domains, but directly constructing utility-aware mechanisms over large or fine-grained domains is often comput…

cs.CR2026

Context-Aware Metric Differential Privacy for Vehicle Trajectory Data

Gaoyi Chen, Yan Huang, Chenxi Qiu

Metric Differential Privacy (mDP) generalizes differential privacy by allowing privacy guarantees to be expressed with respect to an arbitrary distance metric over secrets. While m…

cs.LG2026

Metric-Normalized Posterior Leakage (mPL): Attacker-Aligned Privacy for Joint Consumption

Gaoyi Chen, Minghao Li, Weishi Shi +4

Metric differential privacy (mDP) strengthens local differential privacy (LDP) by scaling noise to semantic distance, but many machine learning (ML) systems are consumed under join…

cs.LG2026

Interpolation-Based Optimization for Enforcing lp-Norm Metric Differential Privacy in Continuous and Fine-Grained Domains

Chenxi Qiu

Metric Differential Privacy (mDP) generalizes Local Differential Privacy (LDP) by adapting privacy guarantees based on pairwise distances, enabling context-aware protection and imp…