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

Domino: exploring mobile collaborative software adaptation

Social Proximity Applications (SPAs) are a promising new area for ubicomp software that exploits the everyday changes in the proximity of mobile users. While a number of applications facilitate simple file sharing between co–present users, this paper explores opportunities for recommending and sharing software between users. We describe an architecture that allows the recommendation of new system components from systems with similar histories of use. Software components and usage histories are exchanged between mobile users who are in proximity with each other. We apply this architecture in a mobile strategy game in which players adapt and upgrade their game using components from other players, progressing through the game through sharing tools and history. More broadly, we discuss the general application of this technique as well as the security and privacy challenges to such an approach

Which One is Me?: Identifying Oneself on Public Displays

While user representations are extensively used on public displays, it remains unclear how well users can recognize their own representation among those of surrounding users. We study the most widely used representations: abstract objects, skeletons, silhouettes and mirrors. In a prestudy (N=12), we identify five strategies that users follow to recognize themselves on public displays. In a second study (N=19), we quantify the users' recognition time and accuracy with respect to each representation type. Our findings suggest that there is a significant effect of (1) the representation type, (2) the strategies performed by users, and (3) the combination of both on recognition time and accuracy. We discuss the suitability of each representation for different settings and provide specific recommendations as to how user representations should be applied in multi-user scenarios. These recommendations guide practitioners and researchers in selecting the representation that optimizes the most for the deployment's requirements, and for the user strategies that are feasible in that environment

Routes for breaching and protecting genetic privacy

We are entering the era of ubiquitous genetic information for research, clinical care, and personal curiosity. Sharing these datasets is vital for rapid progress in understanding the genetic basis of human diseases. However, one growing concern is the ability to protect the genetic privacy of the data originators. Here, we technically map threats to genetic privacy and discuss potential mitigation strategies for privacy-preserving dissemination of genetic data.Comment: Draft for comment