  We propose sanitizer, a framework for secure and task-agnostic data release.
While releasing datasets continues to make a big impact in various applications
of computer vision, its impact is mostly realized when data sharing is not
inhibited by privacy concerns. We alleviate these concerns by sanitizing
datasets in a two-stage process. First, we introduce a global decoupling stage
for decomposing raw data into sensitive and non-sensitive latent
representations. Secondly, we design a local sampling stage to synthetically
generate sensitive information with differential privacy and merge it with
non-sensitive latent features to create a useful representation while
preserving the privacy. This newly formed latent information is a task-agnostic
representation of the original dataset with anonymized sensitive information.
While most algorithms sanitize data in a task-dependent manner, a few
task-agnostic sanitization techniques sanitize data by censoring sensitive
information. In this work, we show that a better privacy-utility trade-off is
achieved if sensitive information can be synthesized privately. We validate the
effectiveness of the sanitizer by outperforming state-of-the-art baselines on
the existing benchmark tasks and demonstrating tasks that are not possible
using existing techniques.
