1,903 research outputs found
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
Privacy in trajectory micro-data publishing : a survey
We survey the literature on the privacy of trajectory micro-data, i.e.,
spatiotemporal information about the mobility of individuals, whose collection
is becoming increasingly simple and frequent thanks to emerging information and
communication technologies. The focus of our review is on privacy-preserving
data publishing (PPDP), i.e., the publication of databases of trajectory
micro-data that preserve the privacy of the monitored individuals. We classify
and present the literature of attacks against trajectory micro-data, as well as
solutions proposed to date for protecting databases from such attacks. This
paper serves as an introductory reading on a critical subject in an era of
growing awareness about privacy risks connected to digital services, and
provides insights into open problems and future directions for research.Comment: Accepted for publication at Transactions for Data Privac
Privacy in trajectory micro-data publishing: a survey
International audienceWe survey the literature on the privacy of trajectory micro-data, i.e., spatiotemporal information about the mobility of individuals, whose collection is becoming increasingly simple and frequent thanks to emerging information and communication technologies. The focus of our review is on privacy-preserving data publishing (PPDP), i.e., the publication of databases of trajectory micro-data that preserve the privacy of the monitored individuals. We classify and present the literature of attacks against trajectory micro-data, as well as solutions proposed to date for protecting databases from such attacks. This paper serves as an introductory reading on a critical subject in an era of growing awareness about privacy risks connected to digital services, and provides insights into open problems and future directions for research
Privacy-Preserving Release of Spatio-temporal Density
International audienceIn today’s digital society, increasing amounts of contextually rich spatio-temporal information are collected and used, e.g., for knowledge-based decision making, research purposes, optimizing operational phases of city management, planning infrastructure networks, or developing timetables for public transportation with an increasingly autonomous vehicle fleet. At the same time, however, publishing or sharing spatio-temporal data, even in aggregated form, is not always viable owing to the danger of violating individuals’ privacy, along with the related legal and ethical repercussions. In this chapter, we review some fundamental approaches for anonymizing and releasing spatio-temporal density, i.e., the number of individuals visiting a given set of locations as a function of time. These approaches follow different privacy models providing different privacy guarantees as well as accuracy of the released anonymized data. We demonstrate some sanitization (anonymization) techniques with provable privacy guarantees by releasing the spatio-temporal density of Paris, in France. We conclude that, in order to achieve meaningful accuracy, the sanitization process has to be carefully customized to the application and public characteristics of the spatio-temporal data
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