G-TOK: Zero-Knowledge Proofs Based Dynamic Verifiable Credentials for Sensor Data Sharing
Junyi Zhong, Thiago Abreu, Sami Souihi, Françoise S. Lucas · 2024
This paper introduces the G-TOK framework, which utilizes advanced zero-knowledge proofs (ZKPs) and dynamic verifiable credentials (VCs) to preserve data privacy in sensor data sharing on blockchain networks. As industries increasingly depend on accurate and confidential sensor data from IoT applications, maintaining privacy and data integrity becomes a significant challenge. Our framework specifically addresses this issue in the context of smart environment sensor networks. Typically, data access control in such networks is either fully permissioned or overly restrictive, lacking mechanisms for selective disclosure access control. Our approach aims to enhance encrypted decentralized data storage and improve data interoperability. It includes dynamic VCs, authenticated privacy-preserving tokenization of geolocation data, and a decentralized real-time location verification scheme. Additionally, we propose a proof-of-footprint (PoF) schema, showcasing the integration and practicality of cutting-edge technologies such as ZKPs, VCs, and self-sovereign identities. This schema aligns with international standards, including the Verifiable Credentials Data Model v2.01and selective disclosure of JSON Web Token (JWT) claims2.