  With the rise of cameras and smart sensors, humanity generates an exponential
amount of data. This valuable information, including underrepresented cases
like AI in medical settings, can fuel new deep-learning tools. However, data
scientists must prioritize ensuring privacy for individuals in these untapped
datasets, especially for images or videos with faces, which are prime targets
for identification methods. Proposed solutions to de-identify such images often
compromise non-identifying facial attributes relevant to downstream tasks. In
this paper, we introduce Disguise, a novel algorithm that seamlessly
de-identifies facial images while ensuring the usability of the modified data.
Unlike previous approaches, our solution is firmly grounded in the domains of
differential privacy and ensemble-learning research. Our method involves
extracting and substituting depicted identities with synthetic ones, generated
using variational mechanisms to maximize obfuscation and non-invertibility.
Additionally, we leverage supervision from a mixture-of-experts to disentangle
and preserve other utility attributes. We extensively evaluate our method using
multiple datasets, demonstrating a higher de-identification rate and superior
consistency compared to prior approaches in various downstream tasks.
