The rapid proliferation of consumer IoT devices is leading to an unprecedented collection of detailed personal data. People are often asked to consent to privacy policies. Most people reflexively choose “I consent” or “I agree”. They agree to unfair-deceptive practices with such uninformed consent, which leads to frustration and privacy resignation (they give up managing their privacy). Choosing “I consent” or “I agree” without reading and understanding the policies becomes increasingly problematic when it is about electronic devices used in daily life, for example, fitness trackers, voice assistants, VR headsets, cleaning robot.
The goal of Privacy@Edge is to develop privacy-assisting solutions that enhance people’s privacy awareness, understandability, and control over their privacy. This will be achieved by extending privacy research with advances in edge computing, natural language processing, LLMs, and privacy-preserving federated learning. In particular, the project leverages edge computing and decentralized machine learning principles to process privacy notices automatically and generate personalized privacy recommendations. A user-friendly privacy dashboard will offer individuals a transparent view of the personal data collected by their IoT devices, and more crucially, the power to effortlessly modify their privacy settings. In essence, Privacy@Edge is not just about technology; it’s about empowering individuals in an increasingly connected world.
The combined experience of SINTEF in privacy and LLM, Norwegian University of Science and Technology in decentralized machine learning and edge computing, & our industry partners Kobla AS in smart cities, and Tellu AS in eHealth services, provides a unique and timely opportunity. Together, we address the pressing challenges of the current 'notice and consent' paradigm, driving transformative solutions in the digital landscape.
