Classification of Datasets Used in Data Anonymization for IoT Environment

The integration of Internet of Things (IoT) devices in smart cities facilitates the collection, sharing, and analysis of data to optimize city services, enhance residents’ well-being, and enable more efficient decision-making processes. Balancing the benefits of IoT data collection and sharing with the imperative of preserving user privacy involves implementing robust security measures and anonymization techniques to ensure the safety, security, and trustworthiness of IoT technologies. Anonymization techniques enable service providers to collect, store, and share data while preserving user privacy, with various methods applicable across different types. This paper presents a literature review focusing on data anonymization in the IoT environment, aiming to classify datasets used to evaluate newly proposed anonymization methods, examining their types, availability, size, and applicability in research. The study aims to provide insights into the datasets employed in data anonymization research for IoT environments and determine the level of using relational datasets in such studies.