Session 3: Personalized Privacy Assistants and Infrastructure for IoT

Even on the wired Web people do not read privacy policies and can’t find time to configure their privacy settings. In the Internet of Things (IoT), this challenge is compounded by the fact that users may not even know what technologies they are interacting with and what settings might be available to them (e.g., opt-in/opt-out). Privacy Assistants are intended to help users manage their privacy, selectively informing them about data practices they would likely want to know about and helping configure any available privacy settings. This session will provide an overview of privacy preference modeling techniques and machine learning techniques designed to drive privacy assistants in the context of mobile and IoT scenarios, including a privacy assistant released on the Google Play Store. It will also provide an overview of an IoT infrastructure developed to help resource owners declare the presence of IoT resources and their privacy policies, help IoT Privacy Assistants discover relevant resources and selectively inform users about their data practices. The infrastructure has been deployed at CMU and UC Irvine. The session will include group exercises to populate IoT resource registries and discuss how this infrastructure can now be deployed by others.