The emergence of resource-rich mobile devices and smart vehicles has paved the way for Urban Sensing. In this new paradigm, users, leveraging their sensing-capable devices, sense their environment and become part of an unprecedented large-scale network of sensors, with extensive spatial and temporal coverage, that enables the collection and dissemination of real-time information, potentially, from anywhere, about anything and at anytime. Urban sensing will facilitate the deployment of innovative applications aiming to address the ever-growing concerns for citizens' well-being by offering a better understanding of our activities and environment.
Nevertheless, the openness of such systems (ideally anyone can participate) and the richness of the data users contribute unavoidably raise significant concerns both about the security of urban sensing applications and the privacy of the participating users. More specifically, users participating in urban sensing applications are expected to contribute sensed data tagged, in many cases, with spatio-temporal information. Misusing such information could reveal sensitive user-specific attributes including their whereabouts, health condition, and habits and lead to extensive and unsolicited user profiling. At the same time, the participation of large numbers of users possessing sensing- capable devices is a double-edged sword: devices can be compromised or faulty or users can be adversarial seeking to manipulate urban sensing systems by submitting intelligently crafted faulty information.
This thesis considers security, resilience and privacy for urban sensing notably in two application domains: intelligent transportation systems and generic smartphone based crowd-sourced sensing applications. For these domains, we design, implement and evaluate provably secure and privacy-preserving solutions capable of protecting the users from the system (i.e., ensuring their privacy in the presence of untrustworthy infrastructure) and the system from malicious users (i.e., holding them accountable for possible system-offending actions)
Stockholm: KTH Royal Institute of Technology, 2016. , 48 p.