Disguise without Disruption: Utility-Preserving Face De-Identification
This addresses privacy concerns for individuals in datasets like medical AI, where face de-identification is crucial but often disrupts utility, though it appears incremental as it builds on differential privacy and ensemble-learning.
The paper tackled the problem of de-identifying facial images without compromising non-identifying attributes for downstream tasks, introducing Disguise, which achieved a higher de-identification rate and superior consistency compared to prior methods.
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.