Differential Privacy on Trust Graphs

• URL: http://arxiv.org/abs/2410.12045v1
• Date: Tue, 15 Oct 2024 20:31:04 GMT
• Title: Differential Privacy on Trust Graphs
• Authors: Badih Ghazi, Ravi Kumar, Pasin Manurangsi, Serena Wang,
• Abstract summary: 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. We give a DP algorithm for aggregation with a much better privacy-utility trade-off than in the well-studied local model of DP.
• Score: 54.55190841518906
• License:
• 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.

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