Biblio

The Internet of Things (IoT) promises to revolutionize the way we interact with the physical world. Even though this paradigm is still far from being completely realized, there already exist Sensing-as-a-Service (S2aaS) platforms that allow users to query for IoT data. While this model offers tremendous benefits, it also entails increasingly challenging privacy issues. In this paper, we concentrate on the protection of user privacy when querying sensing devices through a semi-trusted S2aaS platform. In particular, we build on techniques inspired by proxy re-encryption and k-anonymity to tackle two intertwined problems, namely query privacy and query confidentiality. The feasibility of our solution is validated both analytically and empirically. 

With the ever-increasing number of smart home devices, the issues related to these environments are also growing. With an ever-growing attack surface, there is no standard way to protect homes and their inhabitants from new threats. The inhabitants are rarely aware of the increased security threats that they are exposed to and how to manage them. To tackle this problem, we propose a solution based on segmented architectures similar to the ones used in industrial systems. In this approach, the smart home is segmented into various levels, which can broadly be categorised into an inner level and external level. The external level is protected by a firewall that checks the communication from/to the Internet to/from the external devices. The internal level is protected by an additional firewall that filters the information and the communications between the external and the internal devices. This segmentation guarantees a trusted environment between the entities belonging to the internal network. In this paper, we propose an adaptive trust model that checks the behaviour of the entities and, through this model, in case the entities violate trust rules they can be put in quarantine or banned from the network.