Privacy Preserving Data Provenance Model Based on PUF for Secure Internet of Things

Abstract

Data provenance to maintain data integrity and authenticity is a significant challenge in the Internet of Things (IoT) environments. Additionally, if the provenance metadata itself can be communicated in a privacy-preserving manner, it expands the usage of IoT systems to human societal domains where privacy is of paramount importance. In this paper, we present a scheme to combine data provenance and privacy-preserving solutions. Our scheme merges Physical Unclonable Function (PUF) technology with non-interactive zero-knowledge proof to provide trustworthy and dependable IoT systems. In this context, the IoT device can anonymously send data to the corresponding server associated with the proof of ownership. First, we propose a privacy-preserving data provenance protocol. This protocol was synthesized with Altera Quartus. It was implemented on an Altera Cyclone IV FPGA to demonstrate its practicality and feasibility. Most of the protocol steps take time of the order of 40u sec establishing its practicality.