Differential Privacy Meets Maximum-weight Matching

by   Panayiotis Danassis, et al.

When it comes to large-scale multi-agent systems with a diverse set of agents, traditional differential privacy (DP) mechanisms are ill-matched because they consider a very broad class of adversaries, and they protect all users, independent of their characteristics, by the same guarantee. Achieving a meaningful privacy leads to pronounced reduction in solution quality. Such assumptions are unnecessary in many real-world applications for three key reasons: (i) users might be willing to disclose less sensitive information (e.g., city of residence, but not exact location), (ii) the attacker might posses auxiliary information (e.g., city of residence in a mobility-on-demand system, or reviewer expertise in a paper assignment problem), and (iii) domain characteristics might exclude a subset of solutions (an expert on auctions would not be assigned to review a robotics paper, thus there is no need for indistinguishably between reviewers on different fields). We introduce Piecewise Local Differential Privacy (PLDP), a privacy model designed to protect the utility function in applications where the attacker possesses additional information on the characteristics of the utility space. PLDP enables a high degree of privacy, while being applicable to real-world, unboundedly large settings. Moreover, we propose PALMA, a privacy-preserving heuristic for maximum-weight matching. We evaluate PALMA in a vehicle-passenger matching scenario using real data and demonstrate that it provides strong privacy, ε≤ 3 and a median of ε = 0.44, and high quality matchings (10.8% worse than the non-private optimal).


page 1

page 2

page 3

page 4


Asymmetric Differential Privacy

Recently, differential privacy (DP) is getting attention as a privacy de...

OptimShare: A Unified Framework for Privacy Preserving Data Sharing – Towards the Practical Utility of Data with Privacy

Tabular data sharing serves as a common method for data exchange. Howeve...

Protect Edge Privacy in Path Publishing with Differential Privacy

Paths in a given network are a generalised form of time-serial chains in...

One-sided Differential Privacy

In this paper, we study the problem of privacy-preserving data sharing, ...

Performance Evaluation of Differential Privacy Mechanisms in Blockchain based Smart Metering

The concept of differential privacy emerged as a strong notion to protec...

Differentially Private Multi-Agent Planning for Logistic-like Problems

Planning is one of the main approaches used to improve agents' working e...

Customized Local Differential Privacy for Multi-Agent Distributed Optimization

Real-time data-driven optimization and control problems over networks ma...

Please sign up or login with your details

Forgot password? Click here to reset