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Voting is one of the fundamental pillars of modern democracy. Continuous efforts have been made to strengthen the processes and methods involved to achieve verifiable, transparent voting systems. In recent years, blockchain has been increasingly used to address multi-dimensional challenges across widespread application domains including healthcare, finance and e-voting. However, achieving an efficient solution via use of blockchain requires consideration of a range of factors such as block generation rate, transaction speed, and block size which have a profound role in determining the overall performance of the solution. Current research into this aspect of blockchain is focused on Bitcoin with the objective to achieve comparable performance as of existing online payment systems such as VISA. However, there exists a gap in literature with respect to investigating performance constraints for wider application domains. In this paper, we present our efforts to address this gap by presenting a detailed study into performance and scalability constraints for an e-voting system. Specifically, we conducted rigorous experimentation with permissioned and permissionless blockchain settings across different scenarios with respect to voting population, block size, block generation rate and transaction speed. The experiments highlighted interesting observations with respect to the impact of these parameters on the overall efficiency and scalability of the e-voting model including trade-offs between different parameters as well as security and performance. present our efforts to address this gap by presenting a detailed study into performance and scalability constraints for an e-voting system. Specifically, we conducted rigorous experimentation with permissioned and permissionless blockchain settings across different scenarios with respect to voting population, block size, block generation rate and transaction speed. The experiments highlighted interesting observations with respect to the impact of these parameters on the overall efficiency and scalability of the e-voting model including trade-offs between different parameters as well as security and performance.

The point of departure is the Swedish rebellion against the regime of King John (Hans) in 1501. Sten Sture the Elder was the rebellion's most prominent leader. The article moves from a discussion of Sten's character and motives for his policy to a more general discussion of the motives of the late medieval Scandinavian aristocracy's political agitation and conduct. The principal question is whether the aristocrats were motived by economic profit and personal career alone, or if other motives, like political and ideological ones, also mattered. Several examples of aristocrats' political choices that cannot have been motivated by economic gains are examined. Thereafter, the article presents the main features of the late medieval aristocracy in Scandinavia as an elite, including its political position, and then especially the balance of power between the aristocracy and the monarch. The conclusion is that the late medieval Scandinavian aristocracy's political behaviour was motivated by a set of motives, that could differ from one situation to another. ; acceptedVersion ; © 2019. This is the authors' accepted and refereed manuscript to the article. Locked until 11.11.2021 due to copyright restrictions. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/

Telephony technologies (mobile, VoIP, and fixed) have potentially improved the way we communicate in our daily life and have been widely adopted for business and personal communications. At the same time, scammers, criminals, and fraudsters have also find the telephony network an attractive and affordable medium to target end-users with the advertisement, marketing of legal and illegal products, and bombard them with the huge volume of unwanted calls. These calls would not only trick call recipients into disclosing their private information such as credit card numbers, PIN code which can be used for financial fraud but also causes a lot of displeasure because of continuous ringing. The fraudsters, political campaigners can also use telephony systems to spread malicious information (hate political or religious messages) in real-time through audio or text messages, which have serious political and social consequences if malicious callers are not mitigated in a quick time. In this context, the identification of malicious callers would not only minimize telephony fraud but would also bring peace to the lives of individuals. One way to classifies users as a spammer or legitimate is to get feedback from the call recipients about their recent interactions with the caller, but these systems not only bring inconvenience to callees but also require changes in the system design. The call detail records extensively log the activities of users and can be used to categorize them as the spammer and non-spammer. In this paper, we utilize the information from the call detailed records and proposed a spam detection framework for the telephone network that identifies malicious callers by utilizing the social behavioral features of users within the network. To this extent, we first model the behavior of the users as the directed social graph and then analyze different features of the social graph i.e. the Relationship Network and Call patterns of users towards their peers. We then used these features along with the decision tree to classify callers into three classes i.e. human, spammer and call center. We analyzed the call record data-set consisting of more than 2 million users. We have conducted a detailed evaluation of our framework which demonstrates its effectiveness by achieving acceptable detection accuracy and extremely low false-positive rate. The performance results show that the spammers and call center numbers not only have a large number of non-repetitive calls but also have a large number of short duration calls. Similarly, on the other hand, the legitimate callers have a good number of repetitive calls and most of them interacted for a relatively long duration.

This document represents the final (combined) deliverable for the outcomes of the Privacy as Expected: Consent Gateway (PaE:CG) project. It outlines the project's main deliverables, which consist of: (1) a strategy and vision to offer internet users Consent Receipts and (2) software demonstrating the developed concepts. ; NGI_Trust project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 825618.

Today's smartphones are equipped with a large number of powerful value-added sensors and features such as a low power Bluetooth sensor, powerful embedded sensors such as the digital compass, accelerometer, GPS sensors, Wi-Fi capabilities, microphone, humidity sensors, health tracking sensors, and a camera, etc. These value-added sensors have revolutionized the lives of the human being in many ways such, as tracking the health of the patients and movement of doctors, tracking employees movement in large manufacturing units, and monitoring the environment, etc. These embedded sensors could also be used for large-scale personal, group, and community sensing applications especially tracing the spread of certain diseases. Governments and regulators are turning to use these features to trace the people thought to have symptoms of certain diseases or virus e.g. COVID-19. The outbreak of COVID-19 in December 2019, has seen a surge of the mobile applications for tracing, tracking and isolating the persons showing COVID-19 symptoms to limit the spread of disease to the larger community. The use of embedded sensors could disclose private information of the users thus potentially bring threat to the privacy and security of users. In this paper, we analyzed a large set of smartphone applications that have been designed to contain the spread of the COVID-19 virus and bring the people back to normal life. Specifically, we have analyzed what type of permission these smartphone apps require, whether these permissions are necessary for the track and trace, how data from the user devices is transported to the analytic center, and analyzing the security measures these apps have deployed to ensure the privacy and security of users.