A TAXONOMY OF MACHINE LEARNING-BASED FRAUD DETECTION SYSTEMS

As fundamental changes in information systems drive digitalization, the heavy reliance on computers today significantly increases the risk of fraud. Existing literature promotes machine learning as a potential solution approach for the problem of fraud detection as it is able able to detect patterns in large datasets efficiently. However, there is a lack of clarity and awareness on which components and functionalities of machine learning-based fraud detection systems exist and how these systems can be classified consistently. We draw on 54 identified relevant machine learning-based fraud detection systems to address this research gap and develop a taxonomic scheme. By deriving three archetypes of machine learning-based fraud detection systems, the taxonomy paves the way for research and practice to understand and advance fraud detection knowledge to combat fraud and abuse

Privacy in the Genomic Era

Genome sequencing technology has advanced at a rapid pace and it is now possible to generate highly-detailed genotypes inexpensively. The collection and analysis of such data has the potential to support various applications, including personalized medical services. While the benefits of the genomics revolution are trumpeted by the biomedical community, the increased availability of such data has major implications for personal privacy; notably because the genome has certain essential features, which include (but are not limited to) (i) an association with traits and certain diseases, (ii) identification capability (e.g., forensics), and (iii) revelation of family relationships. Moreover, direct-to-consumer DNA testing increases the likelihood that genome data will be made available in less regulated environments, such as the Internet and for-profit companies. The problem of genome data privacy thus resides at the crossroads of computer science, medicine, and public policy. While the computer scientists have addressed data privacy for various data types, there has been less attention dedicated to genomic data. Thus, the goal of this paper is to provide a systematization of knowledge for the computer science community. In doing so, we address some of the (sometimes erroneous) beliefs of this field and we report on a survey we conducted about genome data privacy with biomedical specialists. Then, after characterizing the genome privacy problem, we review the state-of-the-art regarding privacy attacks on genomic data and strategies for mitigating such attacks, as well as contextualizing these attacks from the perspective of medicine and public policy. This paper concludes with an enumeration of the challenges for genome data privacy and presents a framework to systematize the analysis of threats and the design of countermeasures as the field moves forward

Filtering, Piracy Surveillance and Disobedience

There has always been a cyclical relationship between the prevention of piracy and the protection of civil liberties. While civil liberties advocates previously warned about the aggressive nature of copyright protection initiatives, more recently, a number of major players in the music industry have eventually ceded to less direct forms of control over consumer behavior. As more aggressive forms of consumer control, like litigation, have receded, we have also seen a rise in more passive forms of consumer surveillance. Moreover, even as technology has developed more perfect means for filtering and surveillance over online piracy, a number of major players have opted in favor of “tolerated use,” a term coined by Professor Tim Wu to denote the allowance of uses that may be otherwise infringing, but that are allowed to exist for public use and enjoyment. Thus, while the eventual specter of copyright enforcement and monitoring remains a pervasive digital reality, the market may fuel a broad degree of consumer freedom through the toleration or taxation of certain kinds of activities. This Article is meant largely to address and to evaluate these shifts by drawing attention to the unique confluence of these two important moments: the growth of tolerated uses, coupled with an increasing trend towards more passive forms of piracy surveillance in light of the balance between copyright enforcement and civil liberties. The content industries may draw upon a broad definition of disobedience in their campaigns to educate the public about copyright law, but the market’s allowance of DRM-free content suggests an altogether different definition. The divide in turn between copyright enforcement and civil liberties results in a perfect storm of uncertainty, suggesting the development of an even further division between the role of the law and the role of the marketplace in copyright enforcement and innovation, respectively