A scoping review of personality traits and risk propensity in privacy-sensitive IoT smart homes

The Internet of Things (IoT) is beneficial for smart homes owing to its convenience and efficiency. However, based on a synthesis of 24 peer-reviewed studies, this review revealed a clear research gap in existing privacy-preserving techniques. Because the IoT utilises user-centric devices, the absence of these techniques raises significant privacy concerns, such as unauthorised access and behavioural profiling. Understanding how individual differences in personality and risk propensity shape these behaviours is therefore essential for identifying users at heightened privacy risk. This study aims to investigate personality traits and risk propensity profiles in IoT privacy detection based on users’ privacy behaviours. While no framework currently exists that forecasts a privacy detector model capable of identifying privacy levels, privacy communities, or trait–risk associations across large-scale IoT datasets, this study presents a scoping review that synthesises psychological and technical perspectives on IoT privacy, with a specific focus on how personality traits and risk propensity shape user behaviour and privacy-preserving design. The article also proposes a pioneering privacy-measurement framework that integrates psychological factors with technical safeguards. A key contribution includes actionable strategies for tailoring privacy frameworks to diverse regional and cultural contexts and leveraging emerging technologies such as blockchain and federated learning. Consequently, these findings provide practical guidance for building user trust and advancing user-centric privacy preservation in IoT ecosystems.