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Madiha Tabassum, Tomasz Kosiundefinedski, Alisa Frik, Nathan Malkin, Primal Wijesekera, Serge Egelman, Heather Lipford.  2019.  Investigating Users’ Preferences and Expectations for Always-Listening Voice Assistants. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.. 3(4):23.

Many consumers now rely on different forms of voice assistants, both stand-alone devices and those built into smartphones. Currently, these systems react to specific wake-words, such as "Alexa," "Siri," or "Ok Google." However, with advancements in natural language processing, the next generation of voice assistants could instead always listen to the acoustic environment and proactively provide services and recommendations based on conversations without being explicitly invoked. We refer to such devices as "always listening voice assistants" and explore expectations around their potential use. In this paper, we report on a 178-participant survey investigating the potential services people anticipate from such a device and how they feel about sharing their data for these purposes. Our findings reveal that participants can anticipate a wide range of services pertaining to a conversation; however, most of the services are very similar to those that existing voice assistants currently provide with explicit commands. Participants are more likely to consent to share a conversation when they do not find it sensitive, they are comfortable with the service and find it beneficial, and when they already own a stand-alone voice assistant. Based on our findings we discuss the privacy challenges in designing an always-listening voice assistant.

Pradeep Murukannaiah, Jessica Staddon, Heather Lipford, Bart Knijnenburg.  2016.  PrIncipedia: A Privacy Incidents Encyclopedia. Privacy Law Scholars Conference.

A thorough understanding of society’s privacy incidents is of paramount importance for technical solutions, training/education, social research, and legal scholarship in privacy. The goal of the PrIncipedia project is to provide this understanding by developing the first comprehensive database of privacy incidents, enabling the exploration of a variety of privacy-related research questions. We provide a working definition of “privacy incident” and evidence that it meets end-user perceptions of privacy. We also provide semi-automated support for building the database through a learned classifier that detects news articles about privacy incidents.