On the Usage of Verifiable Credentials in Privacy-Preserving Federated Analytics

2026-09-26

AI Summary

This study addresses the challenges of node authentication, data integrity, and traceability leakage in Federated Privacy-Preserving Analytics (FPPA). To this end, this work introduces verifiable credentials and a self-sovereign identity (SSI) framework into FPPA for the first time, constructing a collaborative model that jointly ensures identity authentication and data integrity. The proposed approach enables multiple parties to derive statistical insights without exchanging raw data, thereby establishing a new paradigm for trusted data interaction. Experimental validation demonstrates the effectiveness of this coupled scheme in guaranteeing node authenticity and data verifiability. Ultimately, this research provides an innovative solution for secure federated computing, advancing both the theoretical foundations and practical deployment of privacy-preserving multi-party analytics.

Abstract

Privacy-Preserving Federated Analytics enables multiple nodes to collaboratively derive statistical insights without exchanging raw data. However, ensuring node authenticity and data validation (avoiding concerns such as identity tracking, data linkage, or data leakage) remain fundamental challenges. This paper offers some insights into the feasibility of defining an authenticated, integrity-preserving data model by coupling FPPA with Verifiable Credentials defined within the Self-Sovereign Identity framework.

Problem

• Privacy-Preserving Federated Analytics
• Verifiable Credentials
• Node Authenticity
• Data Validation
• Self-Sovereign Identity

Innovation

• Privacy-Preserving Federated Analytics
• Verifiable Credentials
• Self-Sovereign Identity
• Data Integrity
• Node Authentication