  The recent rapid advancements in both sensing and machine learning
technologies have given rise to the universal collection and utilization of
people's biometrics, such as fingerprints, voices, retina/facial scans, or
gait/motion/gestures data, enabling a wide range of applications including
authentication, health monitoring, or much more sophisticated analytics. While
providing better user experiences and deeper business insights, the use of
biometrics has raised serious privacy concerns due to their intrinsic sensitive
nature and the accompanying high risk of leaking sensitive information such as
identity or medical conditions.
  In this paper, we propose a novel modality-agnostic data transformation
framework that is capable of anonymizing biometric data by suppressing its
sensitive attributes and retaining features relevant to downstream machine
learning-based analyses that are of research and business values. We carried
out a thorough experimental evaluation using publicly available facial, voice,
and motion datasets. Results show that our proposed framework can achieve a
\highlight{high suppression level for sensitive information}, while at the same
time retain underlying data utility such that subsequent analyses on the
anonymized biometric data could still be carried out to yield satisfactory
accuracy.
