Institute for Software Research
School of Computer Science, Carnegie Mellon University


Understanding People's Privacy Attitudes
Towards Video Analytics Technologies

Shikun Zhang, Yuanyuan Feng, Anupam Das*,
Lujo Bauer, Lorrie Faith Cranor, Norman Sadeh

December 2020


Keywords: Privacy preference modeling, facial recognition, video analytics, experience sampling method

Cameras are everywhere,and are increasingly coupled with video analytics software that can identify our face, track our mood, recognize what we are doing, and more. We present the results of a 10-day in-situ study designed to understand how people feel about these capabilities, looking both at the extent to which they expect to encounter them at venues they visit as part of their everyday activities and at how comfortable they are with the presence of such technologies across a range of realistic scenarios. Results indicate that while some widespread deployments are expected by many (e.g., surveillance in public spaces), others are not, with some making people feel particularly uncomfortable. Our results further show that people's privacy preferences and expectations are complicated and vary with a number of factors such as the purpose for which footage is captured and analyzed, the particular venue where it is captured, or whom it is shared with. Finally, we consider recent technical advances where video analytics can only be used on footage of individuals who consent to it ("opt in"). New regulations such as the General Data Protection Regulation actually mandate obtaining such consent "at or before the point of collection." Because obtaining consent from users at or before each point of collection could result in significant user burden, we use our data to explore the development of predictive models that could one day assist people in managing such consent. Our results are rather encouraging.

42 pages

*Department of Computer Science, North Carolina State University

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