PrivateRide: A Privacy-Enhanced Ride-Hailing Service

In the past few years, we have witnessed a rise in the popularity of ride-hailing services (RHSs), an on-line 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 pro- pose PrivateRide, a privacy-enhancing and practical solu- tion 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 privacy- conscious 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

PrivateRide: A Privacy-Enhanced Ride-Hailing Service

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 privacy-conscious 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

TOPPool: Time-aware Optimized Privacy-Preserving Ridesharing

Ridesharing is revolutionizing the transportation industry in many countries. Yet, the state of the art is based on heavily centralized services and platforms, where the service providers have full possession of the users’ location data. Recently, researchers have started addressing the challenge of enabling privacy-preserving ridesharing. The initial proposals, however, have shortcomings, as some rely on a central party, some incur high performance penalties, and most do not consider time preferences for ridesharing. TOPPool encompasses ridesharing based on the proximity of end-points of a ride as well as partial itinerary overlaps. To achieve the latter, we propose a simple yet powerful reduction to a private set intersection on trips represented as sets of consecutive road segments. We show that TOPPool includes time preferences while preserving privacy and without relying on a third party. We evaluate our approach on real-world data from the New York’s Taxi & Limousine Commission. Our experiments demonstrate that TOPPool is superior in performance over the prior work: our intersection-based itinerary matching runs in less than 0.3 seconds for reasonable trip length, in contrast, on the same set of trips prior work takes up to 10 hours

Technologies respectueuses de la vie privée pour le covoiturage

L'émergence des téléphones mobiles et objets connectés a profondément changé notre vie quotidienne. Ces dispositifs, grâce à la multitude de capteurs qu'ils embarquent, permettent l'accès à un large spectre de services. En particulier, les capteurs de position ont contribué au développement des services de localisation tels que la navigation, le covoiturage, le suivi de la congestion en temps réel... En dépit du confort offert par ces services, la collecte et le traitement des données de localisation portent de sérieuses atteintes à la vie privée des utilisateurs. En effet, ces données peuvent renseigner les fournisseurs de services sur les points d'intérêt (domicile, lieu de travail, orientation sexuelle), les habitudes ainsi que le réseau social des utilisateurs. D'une façon générale, la protection de la vie privée des utilisateurs peut être assurée par des dispositions légales ou techniques. Même si les mesures d'ordre légal peuvent dissuader les fournisseurs de services et les individus malveillants à enfreindre le droit à la vie privée des utilisateurs, les effets de telles mesures ne sont observables que lorsque l'infraction est déjà commise et détectée. En revanche, l'utilisation des technologies renforçant la protection de la vie privée (PET) dès la phase de conception des systèmes permet de réduire le taux de réussite des attaques contre la vie privée des utilisateurs. L'objectif principal de cette thèse est de montrer la viabilité de l'utilisation des PET comme moyens de protection des données de localisation dans les services de covoiturage. Ce type de service de localisation, en aidant les conducteurs à partager les sièges vides dans les véhicules, contribue à réduire les problèmes de congestion, d'émissions et de dépendance aux combustibles fossiles. Dans cette thèse, nous étudions les problèmes de synchronisation d'itinéraires et d'appariement relatifs au covoiturage avec une prise en compte explicite des contraintes de protection des données de localisation (origine, destination). Les solutions proposées dans cette thèse combinent des algorithmes de calcul d'itinéraires multimodaux avec plusieurs techniques de protection de la vie privée telles que le chiffrement homomorphe, l'intersection sécurisée d'ensembles, le secret partagé, la comparaison sécurisée d'entier. Elles garantissent des propriétés de protection de vie privée comprenant l'anonymat, la non-chainabilité et la minimisation des données. De plus, elles sont comparées à des solutions classiques, ne protégeant pas la vie privée. Nos expérimentations indiquent que les contraintes de protection des données privées peuvent être prise en compte dans les services de covoiturage sans dégrader leurs performances.The emergence of mobile phones and connected objects has profoundly changed our daily lives. These devices, thanks to the multitude of sensors they embark, allow access to a broad spectrum of services. In particular, position sensors have contributed to the development of location-based services such as navigation, ridesharing, real-time congestion tracking... Despite the comfort offered by these services, the collection and processing of location data seriously infringe the privacy of users. In fact, these data can inform service providers about points of interests (home, workplace, sexual orientation), habits and social network of the users. In general, the protection of users' privacy can be ensured by legal or technical provisions. While legal measures may discourage service providers and malicious individuals from infringing users' privacy rights, the effects of such measures are only observable when the offense is already committed and detected. On the other hand, the use of privacy-enhancing technologies (PET) from the design phase of systems can reduce the success rate of attacks on the privacy of users. The main objective of this thesis is to demonstrate the viability of the usage of PET as a means of location data protection in ridesharing services. This type of location-based service, by allowing drivers to share empty seats in vehicles, helps in reducing congestion, CO2 emissions and dependence on fossil fuels. In this thesis, we study the problems of synchronization of itineraries and matching in the ridesharing context, with an explicit consideration of location data (origin, destination) protection constraints. The solutions proposed in this thesis combine multimodal routing algorithms with several privacy-enhancing technologies such as homomorphic encryption, private set intersection, secret sharing, secure comparison of integers. They guarantee privacy properties including anonymity, unlinkability, and data minimization. In addition, they are compared to conventional solutions, which do not protect privacy. Our experiments indicate that location data protection constraints can be taken into account in ridesharing services without degrading their performance

CODED SOCIAL CONTROL: CHINA’S NORMALIZATION OF BIOMETRIC SURVEILLANCE IN THE POST COVID-19 ERA

This article investigates the longevity of health QR codes, a digital instrument of pandemic surveillance, in post-COVID China. From 2020 to 2022, China widely used this tri-color tool to combat the COVID-19 pandemic. A commonly held assumption is that health QR codes have become obsolete in post-pandemic China. This study challenges such an assumption. It reveals their persistence and integration - through mobile apps and online platforms - beyond the COVID-19 public health emergency. A prolonged, expanded and normalized use of tools which were originally intended for contact tracing and pandemic surveillance raises critical legal and ethical concerns. Moreover, their functional transformation from epidemiological risk assessment tools to instruments of behavior modification and social governance heralds the emergence of a Data Leviathan. This transformation is underpinned by a duality of underlying political and commercial forces. These include 1) a structural enabler: a powerful alliance between political authorities and tech giants and 2) an ideological legitimizer: a commitment to collective security over individual autonomy. In contrast to the rights-centric approach embraced by Western democracies to regulate AI-driven biometric surveillance, China adopts a state-industry dominance model of governance

Towards Efficient and Secure Intelligent Transportation Services: AI-driven Traffic Light Controller and Privacy-Preserving Mobility Data Generation

The widespread adoption of artificial intelligence (AI) and Intelligent Transportation Systems (ITS) technologies has led to the increasing application of AI-based ITS controllers, with the Traffic Signal Controller (TSC) being a prominent example. Reinforcement learning (RL) models have shown promising results for adaptively adjusting traffic light schedules in urban environments through RL-based TSCs (RL-TSCs). The real-world deployment of RL-TSCs involves three key aspects: performance, security, and data privacy. In terms of performance, RL-TSC models need to be designed with consideration for various metrics, such as fair traffic scheduling and air quality impact. To address this, our approach takes into account a multi-objective constrained learning formulation to optimize performance. However, the use of RL-TSCs for automation, by leveraging external inputs, introduces security concerns that require active research to mitigate. We address these security challenges by introducing an innovative defense mechanism. Additionally, the training of RL-TSCs relies on real-world mobility datasets, necessitating the protection of data privacy at different levels of granularity. To minimize the constraints associated with limited real data availability or privacy concerns, we introduce two distinct directions: synthetic trajectory data generation using recent generative AI methods, and location privacy models for raw mobility datasets based on differential privacy, which safeguard individual trajectories and aggregated mobility datasets.This research provides a valuable tool for evaluating the practical deployment of RL-TSCs, particularly in real-world settings where the last mile of implementation and security is paramount. By addressing the key challenges of performance, security, and data privacy, this work aims to facilitate the successful real-world deployment of AI-powered ITS controllers

LIPIcs, Volume 277, GIScience 2023, Complete Volume

LIPIcs, Volume 277, GIScience 2023, Complete Volum

Varieties of Capitalism: National Institutional Explanations of Environmental Product Developments in the Car Industry

Changing the behaviour of firms to take environmental concerns into account is seen as unlikely without effective regulations. However, corporations are increasingly keen to represent themselves as ‘green’, including those in the world’s largest manufacturing sector: the car industry. Given rising concern for the environment and environmental sustainability since the 1990s this thesis asks: what motivates car firms to actually make environmental commitments? Answering this question has implications for whether these commitments are ‘real’ and if so whether they are occurring in response to material factors (e.g. state regulations and consumer demand) versus normative factors (e.g. social attitudes and internal company strategies). In order to answer it, the thesis applies the insights of the institutional varieties of capitalism approach to the German, United States and Japanese car industries, and specific firms within them, in respect of the environmental issue of climate change from 1990 to 2004. Empirical national data is analysed, as well the environmental reporting of individual firms and interviews with key personnel. The main findings are that what leads the car industry to see environmental issues as central to their business interests hinges on the impact of differing national institutional factors. Specifically, it is a matter of whether firms have a liberal market economy (LME) as their home base, in the case of US firms, or a coordinated market economy (CME) as their home base, in the case of German and Japanese firms. US car firms react more to the material imperatives of consumer demand and state regulations. German and Japanese firms are more mindful of normative factors for their initiatives, such as social attitudes (especially for German firms) and internal company strategies (especially for Japanese firms). They have more of a partnership approach with government. Therefore, car firms have very distinct ‘lenses’ through which they see the environmental performance of the cars they produce. As such, the thesis concludes that the variety of capitalism of nations has implications not just for the type of products that economic actors such as car firms produce, and the competitive advantages they develop, but also the way they address related issues arising as a result of their activities, including environmental issues

Varieties of Capitalism: National Institutional Explanations of Environmental Product Developments in the Car Industry

Changing the behaviour of firms to take environmental concerns into account is seen as unlikely without effective regulations. However, corporations are increasingly keen to represent themselves as ‘green’, including those in the world’s largest manufacturing sector: the car industry. Given rising concern for the environment and environmental sustainability since the 1990s this thesis asks: what motivates car firms to actually make environmental commitments? Answering this question has implications for whether these commitments are ‘real’ and if so whether they are occurring in response to material factors (e.g. state regulations and consumer demand) versus normative factors (e.g. social attitudes and internal company strategies). In order to answer it, the thesis applies the insights of the institutional varieties of capitalism approach to the German, United States and Japanese car industries, and specific firms within them, in respect of the environmental issue of climate change from 1990 to 2004. Empirical national data is analysed, as well the environmental reporting of individual firms and interviews with key personnel. The main findings are that what leads the car industry to see environmental issues as central to their business interests hinges on the impact of differing national institutional factors. Specifically, it is a matter of whether firms have a liberal market economy (LME) as their home base, in the case of US firms, or a coordinated market economy (CME) as their home base, in the case of German and Japanese firms. US car firms react more to the material imperatives of consumer demand and state regulations. German and Japanese firms are more mindful of normative factors for their initiatives, such as social attitudes (especially for German firms) and internal company strategies (especially for Japanese firms). They have more of a partnership approach with government. Therefore, car firms have very distinct ‘lenses’ through which they see the environmental performance of the cars they produce. As such, the thesis concludes that the variety of capitalism of nations has implications not just for the type of products that economic actors such as car firms produce, and the competitive advantages they develop, but also the way they address related issues arising as a result of their activities, including environmental issues