Rural telecommunications policy reform

This publication summarises the issues and points to possible changes in rural telecommunications policy. The purpose was not to revisit the usual arguments about the cost of the universal service obligation, or the comparison between metropolitan areas and remote areas. It simply aims to draw together clear thinking about the issues, in the hope of a better outcome for users and providers of telecommunications

Paying for Privacy and the Personal Data Economy

Growing demands for privacy and increases in the quantity and variety of consumer data have engendered various business offerings to allow companies, and in some instances consumers, to capitalize on these developments. One such example is the emerging “personal data economy” (PDE) in which companies, such as Datacoup, purchase data directly from individuals. At the opposite end of the spectrum, the “pay-for-privacy” (PFP) model requires consumers to pay an additional fee to prevent their data from being collected and mined for advertising purposes. This Article conducts a simultaneous in-depth exploration of the impact of burgeoning PDE and PFP models. It identifies a typology of data-business models, and it uncovers thesimilarities and tensions between a data market controlled by established companies that have historically collected and mined consumer data for their primary benefit and one in which consumers play a central role in monetizing their own data. The Article makes three claims. First, it contends that PFP models facilitate thetransformation of privacy into a tradable product in the online setting, may worsen unequal access to privacy, and could further enable predatory and discriminatory behavior. Second, while the PDE may allow consumers to regain a semblance of control over their information by enabling them to decide when and with whom to share their data, consumers’ direct transfer or disclosure of personal data to companies for a price or personalized deals creates challenges similar to those found in the PFP context and generates additional concerns associated with innovative monetization techniques. Third, existing frameworks and proposals may not sufficiently ameliorate these concerns. The Article concludes by offering a path forward

Online advertising: analysis of privacy threats and protection approaches

Online advertising, the pillar of the “free” content on the Web, has revolutionized the marketing business in recent years by creating a myriad of new opportunities for advertisers to reach potential customers. The current advertising model builds upon an intricate infrastructure composed of a variety of intermediary entities and technologies whose main aim is to deliver personalized ads. For this purpose, a wealth of user data is collected, aggregated, processed and traded behind the scenes at an unprecedented rate. Despite the enormous value of online advertising, however, the intrusiveness and ubiquity of these practices prompt serious privacy concerns. This article surveys the online advertising infrastructure and its supporting technologies, and presents a thorough overview of the underlying privacy risks and the solutions that may mitigate them. We first analyze the threats and potential privacy attackers in this scenario of online advertising. In particular, we examine the main components of the advertising infrastructure in terms of tracking capabilities, data collection, aggregation level and privacy risk, and overview the tracking and data-sharing technologies employed by these components. Then, we conduct a comprehensive survey of the most relevant privacy mechanisms, and classify and compare them on the basis of their privacy guarantees and impact on the Web.Peer ReviewedPostprint (author's final draft

Toward Universal Broadband in Rural Alaska

The TERRA-Southwest project is extending broadband service to 65 communities in the Bristol Bay, Bethel and Yukon-Kuskokwim regions. A stimulus project funded by a combination of grants and loans from the Rural Utilities Service (RUS), TERRA-Southwest has installed a middle-mile network using optical fiber and terrestrial microwave. Last-mile service will be through fixed wireless or interconnection with local telephone networks. The State of Alaska, through its designee Connect Alaska, also received federal stimulus funding from the National Telecommunications and Information Administration (NTIA) for tasks that include support for an Alaska Broadband Task Force “to both formalize a strategic broadband plan for the state of Alaska and coordinate broadband activities across relevant agencies and organizations.” Thus, a study of the impact of the TERRA project in southwest Alaska is both relevant and timely. This first phase provides baseline data on current access to and use of ICTs and Internet connectivity in rural Alaska, and some insights about perceived benefits and potential barriers to adoption of broadband. It is also intended to provide guidance to the State Broadband Task Force in determining how the extension of broadband throughout the state could contribute to education, social services, and economic activities that would enhance Alaska’s future. Results of the research could also be used proactively to develop strategies to encourage broadband adoption, and to identify applications and support needed by users with limited ICT skills.Connect Alaska. The National Telecommunications and Information Administration. General Communications Incorporated.Part 1: An Analysis of Internet Use in Southwest Alaska / Introduction / Previous Studies / Current Connectivity / Analytical Framework and Research Methodology / Demographics / Mobile Phones: Access and Use / Access to the Internet / Internet Useage / Considerations about Internet Service / Interest in Broadband / Sources of News / Comparison with National Data / Internet Use by Businesses and Organizations / What Difference may Broadband make in the Region? / Conclusiongs / Part 2 Literature Review / Reference

Comparing Data Mining Classification Algorithms in Detection of Simbox Fraud

Fraud detection in telecommunication industry has been a major challenge. Various fraud management systems are being used in the industry to detect and prevent increasingly sophisticated fraud activities. However, such systems are rule-based and require a continuous monitoring by subject matter experts. Once a fraudster changes its fraudulent behavior, a modification to the rules is required. Sometimes, the modification involves building a whole new set of rules from scratch, which is a toilsome task that may by repeated many times. In recent years, datamining techniques have gained popularity in fraud detection in telecommunication industry. Unlike rule based Simbox detection, data mining algorithms are able to detect fraud cases when there is no exact match with a predefined fraud pattern, this comes from the fuzziness and the statistical nature that is built into the data mining algorithms. To better understand the performance of data mining algorithms in fraud detection, this paper conducts comparisons among four major algorithms: Boosted Trees Classifier, Support Vector Machines, Logistic Classifier, and Neural Networks. Results of the work show that Boosted Trees and Logistic Classifiers performed the best among the four algorithms with a false-positive ratio less than 1%. Support Vector Machines performed almost like Boosted Trees and Logistic Classifier, but with a higher false-positive ratio of 8%. Neural Networks had an accuracy rate of 60% with a false positive ratio of 40%. The conclusion is that Boosted Trees and Support Vector Machines classifiers are among the better algorithms to be used in the Simbox fraud detections because of their high accuracy and low false-positive ratios

An investigation into customer perception and behaviour through social media research – an empirical study of the United Airline overbooking crisis

Airlines have been adopting yield management to optimise the perishable seat control problem and overbooking is a common strategy. This study outlines the connections between yield management, crises, and crisis communication. Using big data captured on a social media platform, this study aims to combine traditional yield management with emerging social big data analytics. As part of this, we use the twitter data on the 2017 United Airline (UA) to analyse the overbooking crisis. Our findings shed light on the importance of a more effective orchestration of yield management to avoid the escalation of crises during crisis communication phases