Differential Privacy on Trust Graphs

TL;DR

This paper introduces differential privacy algorithms tailored for trust graphs in multi-party settings, achieving improved privacy-utility trade-offs over local models and addressing robustness against untrusted neighbors.

Contribution

It presents novel DP algorithms for trust graphs with better privacy-utility trade-offs and extends to robust settings with untrusted neighbors, supported by lower bounds.

Findings

Enhanced privacy-utility trade-offs in trust graph models

Algorithms for robust trust scenarios with untrusted neighbors

Lower bounds demonstrating limits of privacy in trust graphs

Abstract

We study differential privacy (DP) in a multi-party setting where each party only trusts a (known) subset of the other parties with its data. Specifically, given a trust graph where vertices correspond to parties and neighbors are mutually trusting, we give a DP algorithm for aggregation with a much better privacy-utility trade-off than in the well-studied local model of DP (where each party trusts no other party). We further study a robust variant where each party trusts all but an unknown subset of at most $t$ of its neighbors (where $t$ is a given parameter), and give an algorithm for this setting. We complement our algorithms with lower bounds, and discuss implications of our work to other tasks in private learning and analytics.