在信任图上设计更优的隐私聚合算法，提升数据共享安全性和准确性。
Differential Privacy on Trust Graphs
- 基于信任图结构设计差分隐私聚合机制
- 隐私-效用平衡优于传统本地差分隐私模型
- 适用于多方可信协作的隐私学习场景
我们研究多参与方环境下的差分隐私问题，其中每个参与方仅信任其他方中已知的子集。给定一个信任图（顶点代表参与方，邻居表示相互信任），我们提出一种新的差分隐私聚合算法，在隐私与效用的权衡上显著优于经典的本地差分隐私模型（即每个参与方不信任任何其他方）。此外，我们还研究了一种鲁棒变体：每个参与方信任其所有邻居，除了最多t个未知的不信任者（t为给定参数），并为此设定设计了相应算法。我们还给出了理论下界，并讨论了该工作对其他隐私学习与数据分析任务的启示。
原文摘要 · Abstract (English)
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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