Location Privacy in Spatial Crowdsourcing

Spatial 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 to a set of workers, who will perform the tasks by physically traveling to the tasks' locations. This chapter identifies privacy threats toward both workers and requesters during the two main phases of spatial crowdsourcing, tasking and reporting. Tasking is the process of identifying which tasks should be assigned to which workers. This process is handled by a spatial crowdsourcing server (SC-server). The latter phase is reporting, in which workers travel to the tasks' locations, complete the tasks and upload their reports to the SC-server. The challenge is to enable effective and efficient tasking as well as reporting in SC without disclosing the actual locations of workers (at least until they agree to perform a task) and the tasks themselves (at least to workers who are not assigned to those tasks). This chapter aims to provide an overview of the state-of-the-art in protecting users' location privacy in spatial crowdsourcing. We provide a comparative study of a diverse set of solutions in terms of task publishing modes (push vs. pull), problem focuses (tasking and reporting), threats (server, requester and worker), and underlying technical approaches (from pseudonymity, cloaking, and perturbation to exchange-based and encryption-based techniques). The strengths and drawbacks of the techniques are highlighted, leading to a discussion of open problems and future work

Video annotation by crowd workers with privacy-preserving local disclosure

Advancements in computer vision are still not reliable enough for detecting video content including humans and their actions. Microtask crowdsourcing on task markets such as Amazon Mechnical Turk and Upwork can bring humans into the loop. However, engaging crowd workers to annotate non-public video footage risks revealing the identities of people in the video who may have a right to anonymity. This thesis demonstrates how we can engage untrusted crowd workers to detect behaviors and objects, while robustly concealing the identities of all faces. We developed a web-based system that presents obfuscated videos to crowd workers, and provides them with a mechanism to test their hypotheses about what behaviors and/or objects might be present in the videos. Our system, called Fovea, works by initially applying a heavy median blur to the videos. This guarantees privacy but impedes recognition of other content of interest. An algorithm was developed as a part of this thesis to calculate the radius of a safe-to-reveal region around a pixel. It was implemented into an interactive system that allows workers watching the blurred videos to selectively reveal small regions by clicking. We compared two approaches for local disclosure of information–foveated mode and keyhole mode–together with a non-interactive blur-only mode as a control. The results showed that both modes led to superior recognition of actions while keeping the odds of correct face recognition close to that of the control

Obfuscation and anonymization methods for locational privacy protection : a systematic literature review

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesThe mobile technology development combined with the business model of a majority of application companies is posing a potential risk to individuals’ privacy. Because the industry default practice is unrestricted data collection. Although, the data collection has virtuous usage in improve services and procedures; it also undermines user’s privacy. For that reason is crucial to learn what is the privacy protection mechanism state-of-art. Privacy protection can be pursued by passing new regulation and developing preserving mechanism. Understanding in what extent the current technology is capable to protect devices or systems is important to drive the advancements in the privacy preserving field, addressing the limits and challenges to deploy mechanism with a reasonable quality of Service-QoS level. This research aims to display and discuss the current privacy preserving schemes, its capabilities, limitations and challenges