Today, we are witnessing a trend towards the Internet of Intelligent Things (IoT). The opportunities brought about by such systems can create a game-changing shift in our society. Such opportunities have been enabled mainly as a result of the synergies between IoT technologies and artificial intelligence, through the so-called big data acquired by the IoT devices and analyzed by artificial intelligence and machine learning techniques. One of the main challenges in fully exploiting these recent opportunities is certainly the security and privacy concerns of the users, particularly in the medical and health domain. Such privacy concerns are due to the inherent sensitivity of personal health/medical data, which needs to be analyzed for decision-making by machine-learning algorithms. In this research, our goal is to enable privacy-preserving learning and inference for IoT and mobile-health technologies.

 

This research has been partially supported by the Swedish Research Council (VR) project "Privacy-Preserving Edge Machine Learning on Internet of Things (IoT) Systems with Extreme Resource Constraints", and the Swedish Promobilia Foundation project "Privacy-Preserving Monitoring of COVID-19 Outbreak using Internet of Things (IoT) in Real Time".

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