Predicting User Privacy Preferences based on Dynamic Interpersonal Relationships and Content Sensitivity Analysis
Users are increasingly sharing more self-generated content online. Such content can however endanger users’ privacy and have serious consequences if shared to an inappropriate audience. The current state-of-art to manage the audience of the shared content has repeatedly been demonstrated to be inefficient in appropriately supporting users in this task. In this project, we therefore explore a new approach to assist users in selecting the audience of their content. Our proposal aims at leveraging both the sensitivity of the content to be shared as well as the relationship of the user with the intended audience to make suggestions to users. The ultimate goal of our solution is hence to allow users to simultaneously benefit from sharing content online while better protecting their privacy in a more usable fashion.
LITE – Label for IoT Transparency Enhancement
We provide clear information about the way IoT devices collect and handle your data. Our solution is rooted in the General Data Protection Regulation (GDPR) and has been user validated. Our Label for IoT Transparency Enhancement (LITE) helps protect the privacy of people who use Internet of Things devices. It does so by bringing important privacy facts to the attention of users, whereas normally this info is buried under a ton of paper. Unlike other proposed designs, ours has been validated via usability tests, so we know that non-experts and experts alike can benefit from these labels. The design also considers feedback from security, privacy and legal experts, and it is rooted in the GDPR. The label is augmented with an on-line interface, which provides search and sort functionality, as well as additional visualizations and details that don't fit on a printed label.