Privacy-preserving data analytics in cloud-based smart home with community hierarchy

Abstract

The emergence of the Internet of Things (IoT) has led to increasing data volumes, which are expected to grow exponentially. The IoT has great potential to positively change society but also presents challenges regarding privacy. In practice, Smart community public housing projects involving tens of thousands of households have recently been implemented. This study proposed a privacy-preserving smart home system, which connects a single home controller with data-hiding capabilities through community networking and integrates the data to a hierarchical architecture on a cloud platform for a data analytical access control mechanism. In addition, this paper outlines a variety of smart home data applications through data collected from a smart community environment with the developed privacy protection mechanism. The monitoring and protecting mechanism combines privacy-enhancing technologies with a privacy-preserving strategy from the initial system-designing stage through to full data lifecycle management. The types of smart home data that can be obtained from a community hierarchy, such as identification values, sensitive data, and non-sensitive data, were collected and classified. Through a combination of empirical application and sophisticated exploration of theoretical knowledge, this paper substantially contributes to the home automation field. The proposed system architecture is expected to enable both easy understanding for users and compliance for analytical service providers regarding the operation, procedure, limitation, and benefits of smart home data analysis, thereby providing a solution that ensures both privacy and data availability.