A formal method for privacy‐preservation in cognitive smart cities. Issue 5 (24th October 2021) - Khan, Mohammad Ayoub
Abstract: The Internet of things (IoT) and communication technologies are enabling the consumer to use the smart devices. The explosion of smart devices is shifting the IoT into a framework, which we call the cognitive IoT. The cognitive IoT can enhance many sectors such as smart cities, healthcare, industry 4.0, transportation, just to name a few. Most of the data produced in smart cities are wasted because the important information is not extracted due to lack of standard mechanism for knowledge extraction and archiving methods. This has attracted the attention of researcher to design new approaches of machine and cognitive learning that can handle vast amount of dynamic data. The cognitive smart city is the integration of IoT, smart city technology, real‐time big data analytics and artificial intelligence (AI) strategies for proactive actions. The services in smart cities relies on the collection and analysis of the data which are provided by the use themselves or accessed by the services providers. The citizen engagement is the key for success of smart city; however, the engagement may get reduced due to privacy concerns arising from data collection. Therefore, privacy‐preservation shall be achieved in a manner where valuable data is exchanged with service provider, and other third party while protecting the citizens' privacy, upholding data laws and enforcement. Therefore, there is a need to control the anonymization and mix some more techniques to preserve the quality of the data. The proposed formal method for privacy‐preservation in smart cities is based on pseudonymization, clustering, anonymization and differential privacy methods. The modified clustering algorithm selects the initial cluster based on the concept of dissimilarity between the data sequences. We have assessed the functional correctness and preformation of the proposed model for privacy‐preservation in smart cities. The proposed method has lower discriminating rate as compared to other existing methods.