SemDP: Semantic-level Differential Privacy Protection for Face Datasets
This addresses privacy concerns for individuals in face datasets, but it is incremental as it builds on existing differential privacy approaches.
The paper tackles the problem of protecting privacy in face datasets by proposing a semantic-level differential privacy scheme that applies to the entire dataset, rather than treating each image separately. Experimental results show it maintains visual naturalness and balances privacy-utility trade-offs compared to mainstream methods.
While large-scale face datasets have advanced deep learning-based face analysis, they also raise privacy concerns due to the sensitive personal information they contain. Recent schemes have implemented differential privacy to protect face datasets. However, these schemes generally treat each image as a separate database, which does not fully meet the core requirements of differential privacy. In this paper, we propose a semantic-level differential privacy protection scheme that applies to the entire face dataset. Unlike pixel-level differential privacy approaches, our scheme guarantees that semantic privacy in faces is not compromised. The key idea is to convert unstructured data into structured data to enable the application of differential privacy. Specifically, we first extract semantic information from the face dataset to build an attribute database, then apply differential perturbations to obscure this attribute data, and finally use an image synthesis model to generate a protected face dataset. Extensive experimental results show that our scheme can maintain visual naturalness and balance the privacy-utility trade-off compared to the mainstream schemes.