A Trustworthy Privacy Preserving Framework for Machine Learning in Industrial IoT Systems

【Author】 Arachchige, Pathum Chamikara Mahawaga; Bertok, Peter; Khalil, Ibrahim; Liu, Dongxi; Camtepe, Seyit; Atiquzzaman, Mohammed

【Source】IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS

【影响因子】11.648

【Abstract】Industrial Internet of Things (IIoT) is revolutionizing many leading industries such as energy, agriculture, mining, transportation, and healthcare. IIoT is a major driving force for Industry 4.0, which heavily utilizes machine learning (ML) to capitalize on the massive interconnection and large volumes of IIoT data. However, ML models that are trained on sensitive data tend to leak privacy to adversarial attacks, limiting its full potential in Industry 4.0. This article introduces a framework named PriModChain that enforces privacy and trustworthiness on IIoT data by amalgamating differential privacy, federated ML, Ethereum blockchain, and smart contracts. The feasibility of PriModChain in terms of privacy, security, reliability, safety, and resilience is evaluated using simulations developed in Python with socket programming on a general-purpose computer. We used Ganache_v2.0.1 local test network for the local experiments and Kovan test network for the public blockchain testing. We verify the proposed security protocol using Scyther_v1.1.3 protocol verifier.

【Keywords】Industries; Privacy; Machine learning; Data models; Contracts; Blockchains; differential privacy (DP); ethereum; federated learning (FedML); Industrial Internet of Things (IIoT); Industry 4; 0; IIoT trustworthiness; IPFS; machine learning; smart contract

【发表时间】2020 SEP

【收录时间】2022-01-02

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