Personal Privacy Assistants for the Internet of Things

Anupam Das, Martin Degeling, Daniel Smullen, Norman Sadeh

IEEE Pervasive Computing ( Volume: 17 , Issue: 3 , Jul.-Sep. 2018 )


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. Studies after studies have shown that while people generally care about their privacy, they feel they have little awareness of and 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. Privacy assistants use machine learning to build and refine models of their users' privacy expectations and preferences, selectively inform them about the data practices they care about, and help them configure privacy settings that are available to them. This article focuses on the infrastructure we have developed and fielded to support IoT privacy assistants. The infrastructure enables the assistants to discover IoT resources (e.g., sensors, apps, services and devices) in the vicinity of their users, and selectively inform users about the data practices associated with these resources. The infrastructure also supports the discovery of user-configurable settings for IoT resources (e.g., opt-in, opt-out,data erasure), enabling privacy assistants to help users configure their IoT experience in accordance with their privacy expectations.

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