Toward privacy-enhancing ambulatory-based well-being monitoring: Investigating user re-identification risk in multimodal data

The sensitivity of data collected via ambulatory monitoring, which regularly involve the recording of speech signals and sensor information, can cause strong privacy concerns. We investigate user re-identification risk in a corpus of such data collected to observe the interplay between behavior, physiology, and well-being of healthcare workers in their daily life. We then develop a user anonymization approach that preserves well-being information (i.e., anxiety), but eliminates user identify (ID) information. We formulate this via an auto-encoder that learns a transformed version of the original feature set in an adversarial manner so that it minimizes the anxiety estimation loss and maximizes the user classification loss. Results indicate that the original features bear a large user re-identification risk, while also having a good ability to classify a user's anxiety. After removing the most prone features to user re-identification from the original feature set, the user classification accuracy decreases, while the anxiety classification performance is preserved. The final features transformed via the auto-encoder further reduce evidence of user ID and preserve anxiety classification ability. Findings from this study can contribute to the design privacy-aware bio-behavioral models that can be used for responsible ambulatory monitoring in healthcare and beyond.