SaTC: CORE: Small: A Privacy-Preserving Meta-Data Analysis Framework for Cyber Abuse Research - Foundations, Tools and Algorithms

点击翻译按钮获取中文摘要

This project develops and deploys a mobile communications metadata analysis platform, designed to predict incidents of youth harassment or abuse, drawing on participatory methods to drive research design decisions and maintain youth privacy. By analyzing communication patterns - rather than content - in collaboration with youth participants, this project makes innovative contributions by facilitating a contextualized understanding of youth abuse and harassment. In turn, this analysis supports the development of new, privacy-enhancing tools which allow for early intervention in instances of abuse and distress. Starting from the hypothesis that abused or distressed youth display observably different patterns of communication, the data collected allow researchers to visualize and analyze the everyday communication networks of youth in new ways, providing new opportunities to understand the structures and conditions of youth sociality and forms of abuse while simultaneously developing new predictive models and techniques.In particular, this project 1) designs new methods grounded in participatory design that allows for a better understanding of the ways in which youth conceptualize privacy, adult supervision and surveillance online; 2) develops a highly scalable and practically deployable mobile platform for performing large scale metadata and survey analysis, taking the privacy needs of end users as a core design principle as identified from the first outcome; 3) ensures that the platform guarantees full anonymity to participants, while also allowing for longitudinal and flexible survey administration, metadata collection, and seamless scaling; 4) extracts novel features in the spatial and temporal domain from communication metadata logs (from texts and calls) that associate with cyber harassment and abuse; 5) leverages the features extracted to design context aware machine learning algorithms to quickly detect abnormal patterns from metadata logs. The project trains a number of graduate and undergraduate student in inter-disciplinary research. Long term impact of this project scale well beyond youth populations, and the project more generally moves towards the development of a fully anonymous, confidential, needs-based social scientific research platform.