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Classifying brain signals collected by wearable Internet of Things (IoT)sensors, especially brain-computer interfaces (BCIs), is one of thefastest-growing areas of research. However, research has mostly ignored thesecure storage and privacy protection issues of collected personalneurophysiological data. Therefore, in this article, we try to bridge this gapand propose a secure privacy-preserving protocol for implementing BCIapplications. We first transformed brain signals into images and usedgenerative adversarial network to generate synthetic signals to protect dataprivacy. Subsequently, we applied the paradigm of transfer learning for signalclassification. The proposed method was evaluated by a case study and resultsindicate that real electroencephalogram data augmented with artificiallygenerated samples provide superior classification performance. In addition, weproposed a blockchain-based scheme and developed a prototype on Ethereum, whichaims to make storing, querying and sharing personal neurophysiological data andanalysis reports secure and privacy-aware. The rights of three main transactionbodies - construction workers, BCI service providers and project managers - aredescribed and the advantages of the proposed system are discussed. We believethis paper provides a well-rounded solution to safeguard private data againstcyber-attacks, level the playing field for BCI application developers, and tothe end improve professional well-being in the industry.

A privacy-preserving data storage and service framework based on deep learning and blockchain for construction workers' wearable IoT sensors