Secure authentication and privacy-preserving blockchain for industrial internet of things

【Author】 Sharma, Prakash Chandra; Mahmood, Md Rashid; Raja, Hiral; Yadav, Narendra Singh; Gupta, Brij B.; Arya, Varsha

【Source】COMPUTERS & ELECTRICAL ENGINEERING

【影响因子】4.152

【Abstract】Blockchain (BC) technology has overtaken Industrial Internet of Things (IIoT) platforms. It is necessary to explore efficient implementation. Fault tolerance, decentralised control, authenti-cation, cryptographic security, immutability, data integrity, and BC smart contracts are recom-mended IIoT features. If entities are authenticated and trusted, the internet can be used for industrial activities. Despite several methods, communication is insecure due to scalability, dependability, latency, insufficient transmission security, and uneven data loads. The paper created safe User authentication and optimal BC node selection using AFHENN (Fully Homo-morphic encryption neural network) for IIoT to solve the problem. Mutual authentication, se-crecy, and integrity protect user data. A registration process secures new User authentication. To protect registered data, it uses cryptographic methods like Transient key congruential generator based Elliptic curve cryptography (TKCG-ECC) and Dual keyed Cipolla's Extended Euclidean Algorithm based lattice cryptosystem (DKCEED-LC). To access BCN, the gateway verifies regis-tered users utilising keyed-based Zero Knowledge of Proof (k-ZKP) and Approximation Fully Homomorphic encryption neural network-based Blockchain. Finally, Approximation Fully Ho-momorphic encryption neural network-based Blockchain networking authenticates data (AFHENN-BCN). The BCN avoids legal selection of miner nodes and harmful activities. Compared to top techniques, the proposed work achieves improved throughput and PDR (Packet Delivery Ratio) values with minimal computing time and strong security.

【Keywords】Security; Industrial internet of things; Elliptic curve cryptographycipolla's extended; euclidean algorithm based lattice cryptosystem; Homomorphic encryption neural network; Blockchain

【发表时间】2023 MAY

【收录时间】2023-06-22

【文献类型】理论模型

【主题类别】

区块链应用-实体经济-工业互联网