Efficient privacy-preserving authentication protocol using PUFs with blockchain smart contracts

The Internet of Things devices generates a huge amount of sensitive data. Machine learning is the standard processing paradigm for intelligently handling the huge amount of data. Unfortunately, the IoT devices have limited resources to handle the performance of big data feature learning with machine learning techniques. IoT devices often compromise the privacy of users and make them vulnerable to numerous cyber-attacks. In this paper, we propose an efficient privacy-preserving authentication protocol based on blockchain technology and the secret computational model of physically unclonable function (denoted by PUF model). The proposed protocol guarantees the users privacy with a decentralized smart contract blockchain with the PUF model. In practice, the proposed protocol guarantees that IoT devices and the miner are authenticated in a faster authentication process compared to current blockchain techniques. In addition, Blockchain and PUF combine to ensure data provenance and data transparency in IoT networks. Blockchain-based smart contracts provide decentralized digital ledgers that are able to withstand data tampering attacks. This ensures the security and privacy of outsourced big data in IoT environments. We also investigated the privacy implications of using IoT devices with various security analysis, and avenues for research to extenuate the privacy concerns in IoT environments. (c) 2020 Elsevier Ltd. All rights reserved.