Abstract
The digital economy, online presence and the increasing number of phishing attacks, are common realities in today's operations of a significant number of companies. Some of these attacks resulted in significant financial losses and reputational damage. Companies do not address the problem before- hand. The first step should be the assessment of the exposure of the company to phishing attacks. An assessment methodology is proposed, evaluated and tested using two complete, and real, runs of the methodology.

Abstract
Road pavement conditions influence the daily lives of both drivers and passengers. Anomalies in road pavement can cause discomfort, increase stress, cause mechanical failures in vehicles and compromise safety of road users. Detecting and surveying road condition/anomalies requires expensive and specially designed equipment and vehicles, that cost considerable amounts of money, and require specialized workers to operate them. As an alternative, an emergent sensing paradigm is being discussed as a promising mechanism for collecting large-scale real-world data. In this paper we describe our experience on the design, implementation and deployment of a cloud based road anomaly information management service, that combines Collaborative Mobile Sensing and data-mining approaches, to provide a practical solution for detecting, identifying and managing road anomaly information. Additionally, we identify technical challenges and propose guidelines that may help to improve this type of services and applications. © 2018 IEEE.

Abstract
Anomalies on road pavement cause discomfort to drivers and passengers, and may cause mechanical failure or even accidents. Governments spend millions of Euros every year on road maintenance, often causing traffic jams and congestion on urban roads on a daily basis. This paper analyses the difference between the deployment of a road anomalies detection and identification system in a "conditioned" and a real world setup, where the system performed worse compared to the "conditioned" setup. It also presents a system performance analysis based on the analysis of the training data sets; on the analysis of the attributes complexity, through the application of PCA techniques; and on the analysis of the attributes in the context of each anomaly type, using acceleration standard deviation attributes to observe how different anomalies classes are distributed in the Cartesian coordinates system. Overall, in this paper, we describe the main insights on road anomalies detection challenges to support the design and deployment of a new iteration of our system towards the deployment of a road anomaly detection service to provide information about roads condition to drivers and government entities.

Abstract

Abstract
This paper presents a testbed implementation and evaluation of coding for secrecy schemes in a real environment through software defined radio platforms. These coding schemes rely on interleaving and scrambling with randomly generated keys to shuffle information before transmission. These keys are then encoded jointly with data and then hidden (erased) before transmission, thus only being retrievable through parity information resulting from encoded data. An advantage of the legitimate receiver (e.g. a better signal-to-noise ratio) on the reception of those keys provides the means to achieve secrecy against an adversary eavesdropper. Through this testbed implementation, we show the practical feasibility of coding for secrecy schemes in real-world environments, unveiling the usefulness of interleaving and scrambling with a hidden key to reduce the required advantage over an eavesdropper. We further describe and present solutions to a set of issues that appear when doing practical implementations of security schemes in software defined radio platforms. © 2018 IEEE.

Abstract
Sharing location data is becoming more popular as mobile devices become ubiquitous. Location-based service providers use this type of data to provide geographically contextualized services to their users. However, sharing exact locations with possibly untrustworthy entities poses a thread to privacy. Geo-indistinguishability has been recently proposed as a formal notion based on the concept of differential privacy to design location privacy-preserving mechanisms in the context of sporadic release of location data. While adaptations for the case of continuous location updates have been proposed, the study on how the frequency of updates impacts the privacy and utility level is yet to be made. In this paper we address this issue, by analyzing the effect of frequency updates on the privacy and utility levels of four mechanisms: the standard planar Laplacian mechanism suitable for sparse locations, and three variants of an adaptive mechanism that is an adaptation of the standard mechanism for continuous location updates. Results show that the frequency of updates largely impacts the correlation between points. As the frequency of updates decreases, the correlation also decreases. The adaptive mechanism is able to adjust the privacy and utility levels accordingly to the correlation between past positions and current position. However, the estimator function that is used to predict the current location has a great influence in the obtained results.