Location privacy in spatial crowdsourcing

2018-01-31Spatial crowdsourcing (SC) is a new platform that engages individuals in collecting and analyzing environmental, social and other spatiotemporal information. With SC, requesters outsource their spatiotemporal tasks (tasks associated with location and time) to a set of workers, who will perform the tasks by physically traveling to the tasks' locations. However, current solutions require the workers, who in many cases are simply volunteering for a cause, to disclose their locations to untrustworthy entities. Revealing an individual's location data to other entities may prevent people from contributing to SC applications, thus rendering location privacy a critical obstacle to the growth of SC applications. ❧ This thesis aims to help SC companies (i.e., Uber, TaskRabbit) to protect location privacy of the users (both workers and requesters) participating in their SC platforms. To this end, we identify privacy threats toward both users during the two main phases of SC, tasking and reporting. Tasking (aka task assignment) is the process of identifying which tasks should be assigned to which workers, which is handled by a SC-server. The latter phase is reporting, in which workers travel to the tasks' locations, complete the tasks and upload their reports to the server. Countermeasure studies during the reporting phase have been well studied; hence, we shift our focus on privacy protection during the tasking phase. In order to protect privacy during tasking, the server must assign tasks to workers without having access to the raw locations of the users. We propose privacy-aware frameworks for task assignment in SC as follows. ❧ We first focus on a tasking scenario where the tasks are public and the protection focus is on the workers' locations. We introduce the first framework relies on a trusted third party (TTP) to sanitize the raw location data of the workers. We propose a mechanism based on differential privacy (a mathematically rigorous definition of privacy) and geocasting that achieves effective SC services while offering privacy guarantees to workers. We address scenarios with both static and dynamic (i.e., moving) datasets of workers. Next, we focus on a more realistic scenario where the location privacy of both workers and requesters' tasks need to be protected. We introduce another framework that does not require the TTP---the location data is perturbed locally. We propose a protocol based on geo-indistinguishability (notion of location privacy based on differential privacy) and reachability that achieves effective SC services while offering privacy guarantees to the users. In both scenarios, we investigate analytical/empirical models and task assignment strategies that balance multiple crucial aspects of SC functionality, such as task completion rate, worker travel distance and system overhead