Patron

The Internet of Things (IoT) envisions a world equipped with a huge number of embedded sensors enabling many novel applications like e-health services, real-time manufacturing systems, facility management systems, smart cities, or home automation systems. Many of these IoT applications are based on stream processing systems to transform streams of sensor data into meaningful high-level information like the health status of a user.

Often, the information inferred by stream processing is privacy sensitive. In this project, we investigate a novel powerful access control mechanism for protecting private information in stream processing. The basic idea is to control the access to complex patterns of correlated information, e.g., a pattern of blood pressure, heart rate, and other health-related values identifying a disease of a user of an e-health application. We call these patterns to be concealed from unauthorized access concealed patterns. We investigate different approaches to conceal patterns, which will have different effects on the Quality of Service (QoS) of the system. Our project will cover the whole process from eliciting high-level privacy and QoS requirements, over specifying and realizing concealed patterns in a system, to testing and verifying the conformance to the requirements.