Sherpa.ai Privacy-Preserving Multi-Party Entity Alignment without Intersection Disclosure for Noisy Identifiers

2026-04-21

AI Summary

This work addresses critical limitations in existing entity alignment methods for vertical federated learning, which often leak intersection membership information and struggle to support multi-party settings or tolerate noisy identifiers. The paper proposes the first privacy-preserving set union (PSU) protocol designed explicitly for multi-party scenarios, enabling both exact (ordered) and noise-tolerant (unordered) entity matching. By constructing a shared indexing space grounded in cryptographic secure multi-party computation, the approach accommodates spelling and formatting discrepancies while rigorously preserving the privacy of set intersections. Theoretical analysis confirms its strong security guarantees and low communication complexity, making it particularly suitable for cross-institutional vertical federated learning applications in domains such as healthcare and finance.

Technology Category

• Machine Learning: Privacy
• Natural Language Processing: Language Grounding & Multi-modal NLP
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Security and Privacy: Privacy-enhancing technologies
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Search and Retrieval-Augmented AI: Vertical and domain-specific search

Abstract

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.

Problem

• Privacy-Preserving Entity Alignment
• Vertical Federated Learning
• Private Set Union
• Noisy Identifiers
• Multi-Party Computation

Innovation

• Privacy-Preserving Entity Alignment
• Multi-Party Private Set Union
• Vertical Federated Learning
• Noisy Identifier Matching
• Intersection Privacy