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

TL;DR

This paper presents a decentralized ecosystem for personal data management that enhances user privacy, control, and data verification using advanced privacy technologies like secure enclaves and federated learning.

Contribution

It introduces a novel decentralized architecture that empowers users with complete data ownership and privacy-preserving capabilities across various sectors.

Findings

Supports secure local computation and model training

Enables privacy-preserving data sharing and verification

Ensures data credibility and user privacy

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.