多机构协作时隐藏数据交集，支持有错别字的实体对齐。
Sherpa.ai Privacy-Preserving Multi-Party Entity Alignment without Intersection Disclosure for Noisy Identifiers
- 基于私有集合并集的多方协议，隐藏各方数据交集信息。
- 支持精确匹配与容错匹配，适用于存在拼写错误的数据。
- 适合医疗、金融等需隐私保护的跨机构联合建模场景。
联邦学习（FL）允许多方在不集中原始数据的情况下协作训练模型。垂直联邦学习（VFL）要求在各方拥有相同样本但特征互补的前提下进行，其前提条件是隐私保护实体对齐（PPEA），即在不暴露数据交集的情况下建立样本索引对应关系。传统私有集合交集（PSI）会泄露交集成员身份，而标准私有集合并集（PSU）通过在并集上对齐来缓解该风险。然而，现有方法通常仅限于两方，或缺乏容错匹配能力。本文提出 Sherpa.ai 多方 PSU 协议，一种可隐藏交集成员身份的 PPEA 方法，支持精确与噪声匹配。该协议将两方方案推广至多方，通信开销低，并提供有序（精确对齐）与无序（容忍拼写、格式差异）两种变体。我们证明了协议正确性与隐私性，分析了通信及计算复杂度（指数运算），并形式化定义了从本地记录到共享索引空间的通用映射。该协议为真实世界中的多机构垂直联邦学习（如多医院疾病检测、银行与保险机构联合风控、电信与金融机构跨域反欺诈）提供了可扩展、数学严谨的隐私保护实体对齐方案。
原文摘要 · Abstract (English)
Federated Learning (FL) enables collaborative model training among multiple parties without centralizing raw data. There are two main paradigms in FL: Horizontal FL (HFL), where all participants share the same feature space but hold different samples, and Vertical FL (VFL), where parties possess complementary features for the same set of samples. A prerequisite for VFL training is privacy-preserving entity alignment (PPEA), which establishes a common index of samples across parties (alignment) without revealing which samples are shared between them. Conventional private set intersection (PSI) achieves alignment but leaks intersection membership, exposing sensitive relationships between datasets. The standard private set union (PSU) mitigates this risk by aligning on the union of identifiers rather than the intersection. However, existing approaches are often limited to two parties or lack support for typo-tolerant matching. In this paper, we introduce the Sherpa.ai multi-party PSU protocol for VFL, a PPEA method that hides intersection membership and enables both exact and noisy matching. The protocol generalizes two-party approaches to multiple parties with low communication overhead and offers two variants: an order-preserving version for exact alignment and an unordered version tolerant to typographical and formatting discrepancies. We prove correctness and privacy, analyze communication and computational (exponentiation) complexity, and formalize a universal index mapping from local records to a shared index space. This multi-party PSU offers a scalable, mathematically grounded protocol for PPEA in real-world VFL deployments, such as multi-institutional healthcare disease detection, collaborative risk modeling between banks and insurers, and cross-domain fraud detection between telecommunications and financial institutions, while preserving intersection privacy.
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