AFFIRM: Privacy-by-Design Blockchain for Mobility Data in Web3 using Information Centric Fog Networks with Collaborative Learning

Junaid Ahmed Khan, Kaan Ozbay

Abstract: Micromobility IoT devices and Connected Vehicles generate massive mobility data, crucial for time-critical safety-related data analytics. It is challenging to study and understand such data without compromising user privacy. We propose AFFIRM, a secure privacy-preserving blockchain framework for efficient, scalable and lightweight mobility data generation, validation, storage and retrieval in future Web3 applications. AFFIRM enables nearby devices to self-organize as a fog network and collaboratively train machine learning algorithms locally to securely generate, validate, store and retrieve mobility data via consensus leveraging Information Centric Networking as the underlying architecture. The proposed collaborative learning enables nodes to learn and adapt with respect to parameters related to scalability, timeliness, security, privacy, and resource consumption. We evaluate AFFIRM using mobility data from New York city and results shows it to scalably store mobility data from up to 700 devices with lower delays and overhead.