Conditions for acceptance and usage of mobile payment procedures

Mobile payment (MP) is crucial for, but not limited to mobile commerce. The key to mobile payment acceptance is in the hands of customers. In this paper we examine the conditions for acceptance and actual usage of MP procedures by the customer. We identify essential conditions which belong to the categories costs, security and convenience. Different preferences lead to an individual set of essential conditions for any single user. We propose a scheme for their representation and comparison and, based on these results, examine the relevance of the different criteria with empirical results. Additionally, we propose an approach to a commensurate condition for the usage of MP procedures based on the theory of informational added values. Finally, applications and constrictions of the results are shown and an outlook on the future of mobile payment is given.

Standardized Payment Procedures as Key Enabling Factor for Mobile Commerce

Companies are not going to invest into the development of innovative applications or services unless these can be charged for appropriately. Thus, the existence of standardized and widely accepted mobile payment procedures is crucial for successful business-to-customer mobile commerce. In this paper we reflect on the acceptance of mobile payment and examine the characteristics of current mobile payment procedures. The outcomes of the paper are a categorization of current mobile payment procedures with strategic, participation and operational criteria and, based on these results, the derivation of the five mobile payment standard types prepaid, mobile money, conventional settlement, premium rate number and dual-card. Finally, a prospect is given to possible further development of mobile payment procedures in the direction of an integrative universal mobile payment system (UMPS).

User-centric privacy preservation in Internet of Things Networks

Recent trends show how the Internet of Things (IoT) and its services are becoming more omnipresent and popular. The end-to-end IoT services that are extensively used include everything from neighborhood discovery to smart home security systems, wearable health monitors, and connected appliances and vehicles. IoT leverages different kinds of networks like Location-based social networks, Mobile edge systems, Digital Twin Networks, and many more to realize these services. Many of these services rely on a constant feed of user information. Depending on the network being used, how this data is processed can vary significantly. The key thing to note is that so much data is collected, and users have little to no control over how extensively their data is used and what information is being used. This causes many privacy concerns, especially for a na ̈ıve user who does not know the implications and consequences of severe privacy breaches. When designing privacy policies, we need to understand the different user data types used in these networks. This includes user profile information, information from their queries used to get services (communication privacy), and location information which is much needed in many on-the-go services. Based on the context of the application, and the service being provided, the user data at risk and the risks themselves vary. First, we dive deep into the networks and understand the different aspects of privacy for user data and the issues faced in each such aspect. We then propose different privacy policies for these networks and focus on two main aspects of designing privacy mechanisms: The quality of service the user expects and the private information from the user’s perspective. The novel contribution here is to focus on what the user thinks and needs instead of fixating on designing privacy policies that only satisfy the third-party applications’ requirement of quality of service

TRANSFERRING SECURITY MESSAGE IN TAXI SERVICE IN VEHICULAR AD HOC NETWORK

Taxi service is an important point to point transportation in many cities. One of the major issue have to do with is safety of both the passengers and the taxi drivers. To tackle this problem and to address the certain requirements, we propose a new message transferring scheme for the taxi service. It is based on the theoretical account of the VANET (vehicular Ad-hoc network). Vehicular networks have attracted wide attentions in recent years for their promises. Most of the transmissions in a VANET are via the DSRC wireless interface. For safety purpose the taxi’s OBU uses the pseudo identities instead of real identity for all ongoing transmissions so that a passenger’s travelling route cannot be traced by a third party easily. For communications protocols, our results provide lower message overhead and higher success rate than previous ones. We describe that our schemes are effective in terms of processing delay and message overhead. In detail, our navigation scheme extends to shorter travelling time while our secure taxi service scheme only introduces marginal passenger waiting delay and message overhead

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