Meeting points in ridesharing: A privacy-preserving approach

Abstract

International audienceNowadays, problems of congestion in urban areas due to the massive usage of cars, last-minute travel needs and progress in information and communication technologies have fostered the rise of new transportation modes such as ridesharing. In a ridesharing service, a car owner shares empty seats of his car with other travelers. Recent ridesharing approaches help to identify interesting meeting points to improve the efficiency of the ridesharing service (i.e., the best pickup and drop-off points so that the travel cost is competitive for both driver and rider). In particular, ridesharing services, such as Blablacar or Carma, have become a good mobility alternative for users in their daily life. However, this success has come at the cost of user privacy. Indeed in current's ridesharing services, users are not in control of their own data and have to trust the ridesharing operators with the management of their data. In this paper, we aim at developing a privacy-preserving service to compute meeting points in ridesharing, such that each user remains in control of his location data. More precisely, we propose a decentralized architecture that provides strong security and privacy guarantees without sacrificing the usability of ridesharing services. In particular, our approach protects the privacy of location data of users. Following the privacy-by-design principle, we have integrated existing privacy enhancing technologies and multimodal shortest path algorithms to privately compute mutually interesting meeting points for both drivers and riders in ridesharing. In addition, we have built a prototype implementation of the proposed approach. The experiments, conducted on a real transportation network, have demonstrated that it is possible to reach a trade-off in which both the privacy and utility levels are satisfactory

Real-time city-scale ridesharing via linear assignment problems

“…With the massive adoption of smart phones and social technology, ridesharing services are operating in a dynamic, wide, and articulated environment, and facing a variety of challenges, hence making the research and development of such services a very hot topic in the transport community. For instance, some recent research investigates the advantages of introducing meeting points in terms of cost savings and congestion mitigation (Stiglic et al 2015), the consideration of riders' satisfaction and privacy rights (Avodji et al 2016), the integration of ridesharing in multi-modal systems (Liu et al 2018, Yan et al 2018, the offering of tailored pricing schemes (Sayarshad & Gao 2018) (e.g. for regular travelers such as commuters (Liu & Li 2017, Ma & Zhang 2017), the study of the changes in travel patterns induced by such ridesharing systems (Dong et al 2018), and the consideration of ridesplitting as a binary classification problem (Chen, Zahiri & Zhang 2017).…”

Emerging Privacy Issues and Solutions in Cyber-Enabled Sharing Services: From Multiple Perspectives

“…However, the customers sacrifice their privacy to enjoy the Uber service. The conflict between the disclosure of private information and the service quality becomes more obvious in the ridesharing practices, which is also mentioned in most of the surveyed works, such as [106], [109], [110], [111].…”

“…The exact location must be revealed to a third party middleware Ni et al [108], [109] 2016 Concealing both customers and drivers' sensitive information An anonymous mutual authentication (AMA) protocol A trusted third party middleware is required Aïvodji et al [110] 2016 Computing the mutually interested meeting point…”

An Effective Grouping Method for Privacy-Preserving Bike Sharing Data Publishing

“…User ID and name are never released during bike sharing data publishing. A user's sensitive information might still be leaked due to the presence of location and timing information [17]. Therefore, the requirement of anonymization implies breaking the relations among attribute values, so that a person will be indistinguishable in the released bike sharing transaction microdata table.…”