  Differential privacy (DP) has been the de-facto standard to preserve
privacy-sensitive information in database. Nevertheless, there lacks a clear
and convincing contextualization of DP in image database, where individual
images' indistinguishable contribution to a certain analysis can be achieved
and observed when DP is exerted. As a result, the privacy-accuracy trade-off
due to integrating DP is insufficiently demonstrated in the context of
differentially-private image database. This work aims at contextualizing DP in
image database by an explicit and intuitive demonstration of integrating
conceptional differential privacy with images. To this end, we design a
lightweight approach dedicating to privatizing image database as a whole and
preserving the statistical semantics of the image database to an adjustable
level, while making individual images' contribution to such statistics
indistinguishable. The designed approach leverages principle component analysis
(PCA) to reduce the raw image with large amount of attributes to a lower
dimensional space whereby DP is performed, so as to decrease the DP load of
calculating sensitivity attribute-by-attribute. The DP-exerted image data,
which is not visible in its privatized format, is visualized through PCA
inverse such that both a human and machine inspector can evaluate the
privatization and quantify the privacy-accuracy trade-off in an analysis on the
privatized image database. Using the devised approach, we demonstrate the
contextualization of DP in images by two use cases based on deep learning
models, where we show the indistinguishability of individual images induced by
DP and the privatized images' retention of statistical semantics in deep
learning tasks, which is elaborated by quantitative analyses on the
privacy-accuracy trade-off under different privatization settings.
