Buildings are important elements of cities for VANETs, since these obstacles may attenuate communications between vehicles. Consequently, the impact of buildings has to be considered as part of the attenuation model in VANET simulations of urban scenarios. However, the more elaborated the model, the more information needs to be processed during the simulation, which implies longer processing times. This complexity in simulations is not always worth it, because simplified channel models occasionally offer very accurate results. We compare three approaches to model the impact of buildings in the channel model of simulated VANETs in two urban scenarios. The simulation results for our evaluation scenarios of a traffic-efficiency application indicate that modeling the influence of buildings in urban areas as the total absence of communication between vehicles gives similar results to modeling such influence in a more realistic fashion and could be considered a conservative bound in the performance metrics.

Online tracking is the key enabling technology of modern online advertising. In the recently established model of real-time bidding (RTB), the web pages tracked by ad platforms are shared with advertising agencies (also called DSPs), which, in an auction-based system, may bid for user ad impressions. Since tracking data are no longer confined to ad platforms, RTB poses serious risks to privacy, especially with regard to user profiling, a practice that can be conducted at a very low cost by any DSP or related agency, as we reveal here. In this work, we illustrate these privacy risks by examining a data set with the real ad-auctions of a DSP, and show that for at least 55% of the users tracked by this agency, it paid nothing for their browsing data. To mitigate this abuse, we propose a system that regulates the distribution of bid requests (containing user tracking data) to potentially interested bidders, depending on their previous behavior. In our approach, an ad platform restricts the sharing of tracking data by limiting the number of DSPs participating in each auction, thereby leaving unchanged the current RTB architecture and protocols. However, doing so may have an evident impact on the ad platform's revenue. The proposed system is designed accordingly, to ensure the revenue is maximized while the abuse by DSPs is prevented to a large degree. Experimental results seem to suggest that our system is able to correct misbehaving DSPs, and consequently enhance user privacy.

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Fraud is increasingly common, and so are the losses caused by this phenomenon. There is, thus, an essential economic incentive to study this problem, particularly fraud prevention. One barrier complicating the research in this direction is the lack of public data sets that embed fraudulent activities. In addition, although efforts have been made to detect fraud using machine learning, such actions have not considered the component of human behavior when detecting fraud. We propose a mechanism to detect potential fraud by analyzing human behavior within a data set in this work. This approach combines a predefined topic model and a supervised classifier to generate an alert from the possible fraud-related text. Potential fraud would be detected based on a model built from such a classifier. As a result of this work, a synthetic fraud-related data set is made. Four topics associated with the vertices of the fraud triangle theory are unveiled when assessing different topic modeling techniques. After benchmarking topic modeling techniques and supervised and deep learning classifiers, we find that LDA, random forest, and CNN have the best performance in this scenario. The results of our work suggest that our approach is feasible in practice since several such models obtain an average AUC higher than 0.8. Namely, the fraud triangle theory combined with topic modeling and linear classifiers could provide a promising framework for predictive fraud analysis.

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For a long time, the Internet and web technologies have supported a more fluid interaction between public institutions and citizens through e-government. With this spirit, several public services are being offered online. One of such services, though not a standard one, is transparency. Strongly encouraged by open-data initiatives, transparency is being marketed as a powerful mechanism to fight corruption. Leveraging communication technologies, societies are broadly adopting online transparency practices to give the general public more control over the scrutiny of state institutions. However, a neglected implementation of transparency may cause almost unlimited access to large amounts of information, a side effect we call hyper-transparency. Inevitably, serious privacy risks arise for the individuals in this context. In this work, we analyze the emergence of hyper-transparent practices in Ecuador, a country recently involved in a fierce attempt to offer free access to public information as a fundamental right enabled through e-government. Moreover, we systematically dissect the large amount of microdata released online by Ecuadorian public institutions. Accordingly, we also unveil here a scenario where sensitive information of public employees is openly released under transparency laws. After exposing potential privacy violations, we elaborate on some mechanisms aimed at protecting citizens from such violations.

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