The urgency in the adoption of the Computing Continuum paradigm, which allows computing services to be de-ployed closer to users, requires a plethora of powerful, distributed devices that host such services. In this context, user-owned devices, such as phones or gaming devices, massively distributed by nature and experiencing a continuous growth in computing resources, are naturally fit to host services, and thus, their incorporation to the Continuum to provide the urgently needed infrastructural support is inevitable. In this future, two key challenges must be addressed: automating service orchestration across a massive number of devices, and ensuring device owners maintain agency over the circumstances and conditions under which services can be hosted on their devices and consumed by other users. As a first step towards this future, we present Atmos, an automated service orchestration platform for integrating user-owned devices in the Computing Continuum. Atmos enforces user-defined policies, automatically adjusting the placement and replication of services across devices. The evaluation of Atmos shows that it enforces all user policies, compared to state-of-the-art service orchestration systems, which violate up to 90.3% of user policies, with minimal impact on the experienced QoS.

Opportunistic data sharing allows users to receive real-time, dynamic data directly from peers. These systems not only allow large-scale cooperative sensing but they also empower users to fully control what information is sensed, stored, and shared, enhancing an individual's control over their own potentially private data. While there exist context-aware frameworks that allow individual users to define when and what shared information peers can consume, these approaches have limited expressiveness and do not allow data owners to modulate the granularity of the information released depending on a particular peer or situation. In addition, these frameworks do not consider the consuming peers' privacy, i.e., how much information they have to provide to get access to some desired data. In this paper, we present PADEC, a context-sensitive, privacy-aware framework that allows users to define rich access control rules over their resources and to attach levels of granularity to each rule in order to precisely define who has access to what data when and at what level of detail. Our evaluation shows that PADEC is more expressive than other access control mechanisms and protects the provider’s privacy up to 90% more.

Smartphones have become the perfect companion devices. They have myriad sensors for gathering the context of their owners in order to adapt the behaviour of different applications to the device's situation. This information can also be of great help in enabling the development of social applications that, otherwise, would require a costly and intractable deployment of sensors. Mobile Crowd Sensing systems highly reduce this cost, but realizing this vision using traditional centralized networking primitives requires a constant stream of the sensed data to the cloud in order to store and process it, which in turn leads to the individuals about whom the data is sensed losing control over the privacy of the data. In this paper, we propose an architecture for a device-to-device Mobile Crowd Sensing system and we deepen on a new privacy model that allows users to define access control policies based on their context and the consumer's context.