Privacy preserving data by conceptualizing smart cities using MIDR-Angelization

Abstract

Smart City and IoT improves the performance of health, transportation, energy and reduce the consumption of resources. Among the smart city services, Big Data analytics is one of the imperative technologies that have a vast perspective to reach sustainability, enhanced resilience, effective quality of life and quick management of resources. This paper focuses on the privacy of big data in the context of smart health to support smart cities. Furthermore, the trade-off between the data privacy and utility in big data analytics is the foremost concern for the stakeholders of a smart city. The majority of smart city application databases focus on preserving the privacy of individuals with different disease data. In this paper, we propose a trust-based hybrid data privacy approach named as “MIDR-Angelization” to assure privacy and utility in big data analytics when sharing same disease data of patients in IoT industry. Above all, this study suggests that privacy-preserving policies and practices to share disease and health information of patients having the same disease should consider detailed disease information to enhance data utility. An extensive experimental study performed on a real-world dataset to measure instance disclosure risk which shows that the proposed scheme outperforms its counterpart in terms of data utility and privacy.