  Many computer vision systems require users to upload image features to the
cloud for processing and storage. These features can be exploited to recover
sensitive information about the scene or subjects, e.g., by reconstructing the
appearance of the original image. To address this privacy concern, we propose a
new privacy-preserving feature representation. The core idea of our work is to
drop constraints from each feature descriptor by embedding it within an affine
subspace containing the original feature as well as adversarial feature
samples. Feature matching on the privacy-preserving representation is enabled
based on the notion of subspace-to-subspace distance. We experimentally
demonstrate the effectiveness of our method and its high practical relevance
for the applications of visual localization and mapping as well as face
authentication. Compared to the original features, our approach makes it
significantly more difficult for an adversary to recover private information.
