Related papers: Privacy-preserving Travel Time Prediction with Unc…
Various modern and highly popular applications make use of user data traces in order to offer specific services, often for the purpose of improving the user's experience while using such applications. However, even when user data is…
When it comes to location-based services (LBS), user privacy protection can be in conflict with security of both users and trips. While LBS providers could adopt privacy preservation mechanisms to obfuscate customer data, the accuracy of…
Travel time prediction is central to transport geography and planning's accessibility analyses, sustainable transportation infrastructure provision, and active transportation interventions. However, calculating accurate travel times,…
With the tremendous increase in the number of smart phones, app stores have been overwhelmed with applications requiring geo-location access in order to provide their users better services through personalization. Revealing a user's…
Recent statistical methods fitted on large-scale GPS data can provide accurate estimations of the expected travel time between two points. However, little is known about the distribution of travel time, which is key to decision-making…
The success of applications for sharing GPS trajectories raises serious privacy concerns, in particular about users' home addresses. In this paper we show that a Bayesian approach is natural and effective for a rigorous analysis of…
We study six months of human mobility data, including WiFi and GPS traces recorded with high temporal resolution, and find that time series of WiFi scans contain a strong latent location signal. In fact, due to inherent stability and low…
Contact tracing is being widely employed to combat the spread of COVID-19. Many apps have been developed that allow for tracing to be done automatically based off location and interaction data generated by users. There are concerns,…
This study is about harvesting point of interest from GPS trajectories. Trajectories are the paths that moving objects move by follow through space in a function of time while GPS trajectories generally are point-sequences with geographic…
The recent SARS-CoV-2 pandemic gave rise to management approaches using mobile apps for contact tracing. The corresponding apps track individuals and their interactions, to facilitate alerting users of potential infections well before they…
Preserving user privacy is paramount when it comes to publicly disclosed datasets that contain fine-grained data about large populations. The problem is especially critical in the case of mobile traffic datasets collected by cellular…
With the popularization of different kinds of smart terminals and the development of autonomous driving technology, more and more services based on spatio-temporal data have emerged in our lives, such as online taxi services, traffic flow…
Sharing sensitive data is vital in enabling many modern data analysis and machine learning tasks. However, current methods for data release are insufficiently accurate or granular to provide meaningful utility, and they carry a high risk of…
Contact tracing in the COVID-19 pandemic is key to prevent the further spread of COVID-19. Countries and regions around the world have developed and deployed or are considering adopting contact-tracing software or mobile apps. While contact…
Contact tracing has been considered as an effective measure to limit the transmission of infectious disease such as COVID-19. Trajectory-based contact tracing compares the trajectories of users with the patients, and allows the tracing of…
The COVID-19 pandemic and the implementation of social distancing policies have rapidly changed people's visiting patterns, as reflected in mobility data that tracks mobility traffic using location trackers on cell phones. However, the…
Mobile devices with rich features can record videos, traffic parameters or air quality readings along user trajectories. Although such data may be valuable, users are seldom rewarded for collecting them. Emerging digital marketplaces allow…
The rapid growth of the Internet of Things (IoT) necessitates employing privacy-preserving techniques to protect users' sensitive information. Even when user traces are anonymized, statistical matching can be employed to infer sensitive…
Many popular applications use traces of user data to offer various services to their users. However, even if user data is anonymized and obfuscated, a user's privacy can be compromised through the use of statistical matching techniques that…
As our ground transportation infrastructure modernizes, the large amount of data being measured, transmitted, and stored motivates an analysis of the privacy aspect of these emerging cyber-physical technologies. In this paper, we consider…