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Blockchain-enabled Privacy Preserving of IoT Data for Sustainable Smart Cities using Machine Learning

The development of sensor technologies and an explosion of the inexpensive electronic circuit, the Internet of Things (IoT) is emergent as an encouraging innovation to comprehend sustainable smart city. Smart cities can bid various intelligent applications like smart transportation, smart banking, and industry 4.0, among others, to boost citizens' life quality. However, security is one of the critical problems of a smart city. These emerging smart infrastructure and applications based on IoT can benefit users only if vital private and secure features are guaranteed. Hence, in this paper, Blockchain-enabled Privacy-Preserving Access Control System (BPACS) has been suggested for IoT data in a smart city environment. This study utilizes blockchain methods to construct a reliable and secure data-sharing policy between numerous data providers, where IoT information is encoded and then verified on disseminated ledgers. Furthermore, this study design protected construction blocks, like secure comparison and secure polynomial multiplications, by retaining cryptosystems and build a secure Support Vector Machine (SVM) and Principle Component Analysis (PCA) training algorithms. Hard security analysis proves that the suggested model guarantees the privacy of the sensitive information for every data provider and the SVM and PCA model variables for data analysts.