A User-Centric, Privacy-Preserving, and Verifiable Ecosystem for Personal Data Management and Utilization

2025-06-27

AI Summary

Centralized personal data management poses significant privacy breaches, security vulnerabilities, and erosion of user autonomy—particularly inadequate for highly sensitive domains such as education, healthcare, and finance, where stringent regulatory compliance is required. To address these challenges, this paper proposes a user-centric, decentralized data management paradigm that enables selective data sharing and end-to-end privacy preservation. Our approach innovatively integrates verifiable credentials with fine-grained, policy-based access control to unambiguously establish and enforce data ownership. Furthermore, we synergistically combine trusted execution environments (TEEs) with federated learning to support secure local computation, collaborative model training, and privacy-preserving cross-domain data exchange. Experimental evaluation demonstrates the system’s feasibility, robust security guarantees—including strong confidentiality and integrity under adversarial settings—and scalability across heterogeneous deployments.

Technology Category

• Machine Learning: Privacy
• Data Mining & Knowledge Management: Scalability, Parallel & Distributed Systems
• Application Domains: Security

Application Category

• Security and Privacy: Data transparency and provenance
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Semantics and Knowledge: Provenance, trust, security and privacy, and ethical issues in managing semantic data

Abstract

In the current paradigm of digital personalized services, the centralized management of personal data raises significant privacy concerns, security vulnerabilities, and diminished individual autonomy over sensitive information. Despite their efficiency, traditional centralized architectures frequently fail to satisfy rigorous privacy requirements and expose users to data breaches and unauthorized access risks. This pressing challenge calls for a fundamental paradigm shift in methodologies for collecting, storing, and utilizing personal data across diverse sectors, including education, healthcare, and finance. This paper introduces a novel decentralized, privacy-preserving architecture that handles heterogeneous personal information, ranging from educational credentials to health records and financial data. Unlike traditional models, our system grants users complete data ownership and control, allowing them to selectively share information without compromising privacy. The architecture's foundation comprises advanced privacy-enhancing technologies, including secure enclaves and federated learning, enabling secure computation, verification, and data sharing. The system supports diverse functionalities, including local computation, model training, and privacy-preserving data sharing, while ensuring data credibility and robust user privacy.

Problem

• Centralized personal data management raises privacy and security concerns
• Traditional architectures fail to meet privacy and autonomy requirements
• Need decentralized system for secure, verifiable data sharing and control

Innovation

• Decentralized architecture for personal data management
• Secure enclaves and federated learning technologies
• User-controlled selective data sharing without privacy loss