Personalized Privacy Assistants for the Internet of Things: Providing Users with Notice and Choice

Abstract

As we interact with an increasingly diverse set of sensing technologies, it becomes difficult to keep up with the many different ways in which data about ourselves is collected and used. Study after study has shown that while people generally care about their privacy, they feel they have little awareness of-let alone control over-the collection and use of their data. This article summarizes ongoing research to develop and field privacy assistants designed to empower people to regain control over their privacy in the Internet of Things (IoT). Specifically, we focus on the infrastructure we have developed and deployed to support privacy assistants for the IoT. This infrastructure enables the assistants to discover IoT resources (sensors, apps, services, devices, and so on) in the vicinity of their users, and selectively inform users about the data practices associated with these resources. It also supports the discovery of user-configurable settings for IoT resources (opt in, opt out, data erasure, and so on) if there are any, enabling privacy assistants to help users configure their IoT experience in accordance with their privacy expectations. We also discuss how, using machine learning to build and refine models of users privacy expectations and preferences, we plan to developed personalized privacy assistants capable of selectively informing their users about the data practices they actually care about and of helping them configure associated privacy settings.