Honorable Mention
Users avoid engaging with privacy policies because they are lengthy and complex, making it challenging to retrieve relevant information. In response, research proposed contextual privacy policies (CPPs) that embed relevant privacy information directly into their affiliated contexts. To date, CPPs are limited to concept showcases. This work evolves CPPs into a production tool that automatically extracts and displays concise policy information. We first evaluated the technical functionality on the US's 500 most visited websites with 59 participants. Based on our results, we further revised the tool to deploy it in the wild with 11 participants over ten days. We found that our tool is effective at embedding CPP information on websites. Moreover, we found that the tool's usage led to more reflective privacy behavior, making CPPs powerful in helping users understand the consequences of their online activities. We contribute design implications around CPP presentation to inform future systems design.
https://dl.acm.org/doi/abs/10.1145/3491102.3517688
The ACM CHI Conference on Human Factors in Computing Systems (https://chi2022.acm.org/)