Enabling query answering in a trustworthy privacy-aware spatial crowdsourcing

With the ubiquity of mobile devices, spatial crowdsourcing is emerging as a new platform, enabling spatial tasks (i.e., tasks related to a location) assigned to and performed by human workers. However, privacy and trust are the two significant barriers to the success of any spatial crowdsourcing system. First, the workers may not want to associate themselves with the task they perform. Second, the validity of the contributed data is not verified, since the intentions of the workers is not always clear. In this dissertation, for the first time we introduce a taxonomy for spatial crowdsourcing. Subsequently, we study one class of this taxonomy, in which workers send their locations to a centralized server and thereafter the server assigns to every worker his nearby tasks. Thereafter, we formally define the problem of privacy and trust in spatial crowdsourcing systems and examine its challenges. We propose a trustworthy privacy-aware framework for spatial crowdsourcing systems, which enables the participation of the workers without compromising their privacy while improving the trustworthiness of the performed tasks.