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.
@article{arxiv.2410.12045,
title = {Differential Privacy on Trust Graphs},
author = {Badih Ghazi and Ravi Kumar and Pasin Manurangsi and Serena Wang},
journal= {arXiv preprint arXiv:2410.12045},
year = {2024}
}