Trust me, I’m an Intermediary! Exploring Data Intermediation Services

Data ecosystems receive considerable attention in academia and practice, as indicated by a steadily growing body of research and large-scale (industry-driven) research projects. They can leverage so-called data intermediaries, which are mediating parties that facilitate data sharing between a data provider and a data consumer. Research has uncovered many types of data intermediaries, such as data marketplaces or data trusts. However, what is missing is a ‘big picture’ of data intermediaries and the functions they fulfill. We tackle this issue by extracting data intermediation services decoupled from specific instances to give a comprehensive overview of how they work. To achieve this, we report on a systematic literature review, contributing data intermediation services

Who is Reading Whom Now: Privacy in Education from Books to MOOCs

This Article is the most comprehensive study to date of the policy issues and privacy concerns arising from the surge of ed tech innovation. It surveys the burgeoning market of ed tech solutions, which range from free Android and iPhone apps to comprehensive learning management systems and digitized curricula delivered via the Internet. It discusses the deployment of big data analytics by education institutions to enhance student performance, evaluate teachers, improve education techniques, customize programs, and better leverage scarce resources to optimize education results. This Article seeks to untangle ed tech privacy concerns from the broader policy debates surrounding standardization, the Common Core, longitudinal data systems, and the role of business in education. It unpacks the meaning of commercial data uses in schools, distinguishing between behavioral advertising to children and providing comprehensive, optimized education solutions to students, teachers, and school systems. It addresses privacy problems related to small data --the individualization enabled by optimization solutions that read students even as they read their books-as well as concerns about big data analysis and measurement, including algorithmic biases, discreet discrimination, narrowcasting, and chilling effects. This Article proposes solutions ranging from deployment of traditional privacy tools, such as contractual and organizational governance mechanisms, to greater data literacy by teachers and parental involvement. It advocates innovative technological solutions, including converting student data to a parent-accessible feature and enhancing algorithmic transparency to shed light on the inner working of the machine. For example, individually curated data backpacks would empower students and their parents by providing them with comprehensive portable profiles to facilitate personalized learning regardless of where they go. This Article builds on a methodology developed in the authors\u27 previous work to balance big data rewards against privacy risks, while complying with several layers of federal and state regulation

An Approach to Guide Users Towards Less Revealing Internet Browsers

When browsing the Internet, HTTP headers enable both clients and servers send extra data in their requests or responses such as the User-Agent string. This string contains information related to the sender’s device, browser, and operating system. Previous research has shown that there are numerous privacy and security risks result from exposing sensitive information in the User-Agent string. For example, it enables device and browser fingerprinting and user tracking and identification. Our large analysis of thousands of User-Agent strings shows that browsers differ tremendously in the amount of information they include in their User-Agent strings. As such, our work aims at guiding users towards using less exposing browsers. In doing so, we propose to assign an exposure score to browsers based on the information they expose and vulnerability records. Thus, our contribution in this work is as follows: first, provide a full implementation that is ready to be deployed and used by users. Second, conduct a user study to identify the effectiveness and limitations of our proposed approach. Our implementation is based on using more than 52 thousand unique browsers. Our performance and validation analysis show that our solution is accurate and efficient. The source code and data set are publicly available and the solution has been deployed