With the emergence of IoT and a data-centric economy, where a growing number of products, services and business processes rely on the collection and processing of user data, people are increasingly confronted to an unmanageable number of privacy decisions. While there is ample evidence that people care about their privacy, research shows that they are simply overwhelmed by the amount of information they would have to read and settings they are expected to configure. Our team has been developing and piloting personalized privacy assistants, namely intelligent assistants capable of learning the privacy preferences of their users over time to selectively inform them about data collection and use practices they would want to know about and to help them discover and configure available settings.

An Infrastructure for Privacy Notifaction and Choice in IoT

This involves the development of an infrastructure and protocols to help privacy assistants discover relevant IoT resources, relevant elements of their privacy policies and any available privacy settings. This also includes developing models of people’s privacy preferences and expectations, including notification preferences as they pertain to a growing collection of IoT scenarios.

Deployments Outside Carnegie Mellon

Our IoT Privacy Infrastructure and Personalized Privacy Assistants are intended to be deployed in a variety of environments - not just at CMU. Watch a video of our technology as deployed at UC Irvine, where it has been deployed to interface with UCI's IoT Tippers testbed.