8 papers
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-…
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…
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…
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…
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…
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…
PAnDA: Rethinking Metric Differential Privacy Optimization at Scale with Anchor-Based Approximation
Ruiyao Liu, Chenxi Qiu
Metric Differential Privacy (mDP) extends the local differential privacy (LDP) framework to metric spaces, enabling more nuanced privacy protection for data such as geo-locations.…
Enhancing Scalability of Metric Differential Privacy via Secret Dataset Partitioning and Benders Decomposition
Chenxi Qiu
Metric Differential Privacy (mDP) extends the concept of Differential Privacy (DP) to serve as a new paradigm of data perturbation. It is designed to protect secret data represente…