Abstract:In the past few years, we have witnessed a rise in the popularity of ride-hailing services (RHSs), an online marketplace that enables accredited drivers to use their own cars to drive ride-hailing users. Unlike other transportation services, RHSs raise significant privacy concerns, as providers are able to track the precise mobility patterns of millions of riders worldwide. We present the first survey and analysis of the privacy threats in RHSs. Our analysis exposes high-risk privacy threats that do not occur in conventional taxi services. Therefore, we propose PrivateRide, a privacy-enhancing and practical solution that offers anonymity and location privacy for riders, and protects drivers' information from harvesting attacks. PrivateRide lowers the high-risk privacy threats in RHSs to a level that is at least as low as that of many taxi services. Using real data-sets from Uber and taxi rides, we show that PrivateRide significantly enhances riders' privacy, while preserving tangible accuracy in ride matching and fare calculation, with only negligible effects on convenience. Moreover, by using our Android implementation for experimental evaluations, we show that PrivateRide's overhead during ride setup is negligible. In short, we enable privacyconscious riders to achieve levels of privacy that are not possible in current RHSs and even in some conventional taxi services, thereby offering a potential business differentiator.

Activity-based social networks, where people upload and share information about their location-based activities (e.g., the routes of their activities), are increasingly popular. Such systems, however, raise privacy and security issues: the service providers know the exact locations of their users; the users can report fake location information to, for example, unduly brag about their performance. In this paper, we propose a secure privacy-preserving system for reporting location-based activity summaries (e.g., the total distance covered and the elevation gain). Our solution is based on a combination of cryptographic techniques and geometric algorithms, and it relies on existing Wi-Fi access point networks deployed in urban areas. We evaluate our solution by using real data-sets from the FON community networks and from the Garmin Connect activity-based social network, and show that it can achieve tight (up to a median accuracy of 79%) verifiable lower-bounds of the distance covered and of the elevation gain, while protecting the location privacy of the users with respect to both the social network operator and the access point network operator(s).

Abstract-Activity-tracking applications, where people record and upload information about their location-based activities (e.g., the routes of their activities), are increasingly popular. Such applications enable users to share information and compete with their friends on activity-based social networks but also, in some cases, to obtain discounts on their health insurance premiums by proving they conduct regular fitness activities. However, they raise privacy and security issues: the service providers know the exact locations of their users; the users can report fake location information, for example, to unduly brag about their performance. In this paper, we present SecureRun, a secure privacy-preserving system for reporting location-based activity summaries (e.g., the total distance covered and the elevation gain). SecureRun is based on a combination of cryptographic techniques and geometric algorithms, and it relies on existing Wi-Fi access-point networks deployed in urban areas. We evaluate SecureRun by using real data-sets from the FON hotspot community networks and from the Garmin Connect activity-based social network, and we show that it can achieve tight (up to a median accuracy of more than 80%) verifiable lower-bounds of the distance covered and of the elevation gain, while protecting the location privacy of the users with respect to both the social network operator and the access point network operator(s). The results of our online survey, targeted at RunKeeper users recruited through the Amazon Mechanical Turk platform, highlight the lack of awareness and significant concerns of the participants about the privacy and security issues of activity-tracking applications. They also show a good level of satisfaction regarding SecureRun and its performance.

SimGridMC (also dubbed Mc SimGrid) is a stateful Model Checker for MPI applications. It is integrated to SimGrid, a framework mostly dedicated to predicting the performance of distributed applications. We describe the architecture of McSimGrid, and show how it copes with the state space explosion problem using Dynamic Partial Order Reduction and State Equality algorithms. As case studies we show how SimGrid can enforce safety and liveness properties for MPI applications, as well as global invariants over communication patterns.

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