  There is growing concern about image privacy due to the popularity of social
media and photo devices, along with increasing use of face recognition systems.
However, established image de-identification techniques are either too subject
to re-identification, produce photos that are insufficiently realistic, or
both. To tackle this, we present a novel approach for image obfuscation by
manipulating latent spaces of an unconditionally trained generative model that
is able to synthesize photo-realistic facial images of high resolution. This
manipulation is done in a way that satisfies the formal privacy standard of
local differential privacy. To our knowledge, this is the first approach to
image privacy that satisfies $\varepsilon$-differential privacy \emph{for the
person.}
