提出高阶联邦U统计量安全计算新方法，精度显著提升。
Accurate, private, secure, federated U-statistics with higher degree
- 用多方计算实现高阶U统计量的中心差分隐私计算
- 对Kendall's τ系数，均方误差降低四个数量级
- 适合需要高精度隐私保护统计分析的场景
我们研究在联邦学习设置下计算度数为k≥2的U统计量（即函数f在所有k元组上的平均值）的问题。二阶U统计量包括肯德尔τ系数、曲线下面积和吉尼均差等常用统计量。现有方法仅适用于低效的本地差分隐私模型，或在域离散化规模增大时性能急剧下降。本文提出一种基于多方计算（MPC）的协议，在中心差分隐私下安全计算任意k≥2阶的U统计量。该方法在准确率上显著优于已有方案。我们提供了详细的理论分析，涵盖精度、通信与计算开销。实验评估表明，对于肯德尔τ系数，我们的方法相比基线可将均方误差降低多达四个数量级。
原文摘要 · Abstract (English)
We study the problem of computing a U-statistic with a kernel function f of degree k $\ge$ 2, i.e., the average of some function f over all k-tuples of instances, in a federated learning setting. Ustatistics of degree 2 include several useful statistics such as Kendall's $τ$ coefficient, the Area under the Receiver-Operator Curve and the Gini mean difference. Existing methods provide solutions only under the lower-utility local differential privacy model and/or scale poorly in the size of the domain discretization. In this work, we propose a protocol that securely computes U-statistics of degree k $\ge$ 2 under central differential privacy by leveraging Multi Party Computation (MPC). Our method substantially improves accuracy when compared to prior solutions. We provide a detailed theoretical analysis of its accuracy, communication and computational properties. We evaluate its performance empirically, obtaining favorable results, e.g., for Kendall's $τ$ coefficient, our approach reduces the Mean Squared Error by up to four orders of magnitude over existing baselines.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。