Abstract

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 …

Date

2023

Authors

Ravi Pranjal, Ranjana Seshadri, Rakesh Kumar Sanath Kumar Kadaba, Tiantian Feng, Shrikanth S Narayanan, Theodora Chaspari

Conference

ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Pages

1-5

Publisher

IEEE