A Privacy-Preserving Data Aggregation Scheme for Fog/Cloud-Enhanced IoT Applications Using a Trusted Execution Environment

The use of IoT devices is increasingly present in our daily lives, as they offer many possibilities for developers and the industry to develop applications, taking advantage of their connectivity capabilities, low cost, and, often, small size. As the use of these applications is continuously increasing, the concerns about the privacy and confidentiality of the data generated by these devices also increase, since many applications share the collected data with fog and cloud servers, due to the computational constraints of the edge devices. Fog and cloud environments are used to aggregate and analyze data collected by multiple devices, allowing to summarize these data and to offer personalized services to the users. As IoT devices can collect sensitive data from users, such as personal and behavioral information, it is crucial to handle such data ensuring the privacy of their owners. Privacy-preserving data aggregation schemes are proposed in the literature, but many of them are limited to specific functions and homogeneous data or to specific contexts, such as smart metering and e-health, and there is no publicly available tool to handle heterogeneous data. This paper describes ongoing research that aims to build a generic data aggregation scheme, taking advantage of Trusted Execution Environments (TEE) to ensure data and user privacy and allowing to process heterogeneous data and perform complex computations, including the use of machine learning algorithms. We describe the system architecture, our preliminary findings, and the next steps to implement and validate our proposal.