Generative BlendPose-LDM: Utility-preserving face image anonymization via k-anonymous latent diffusion

Face image anonymization aims to preclude re-identification while preserving images’ analytical utility and visual quality. Although existing image anonymization approaches can conceal identity cues of faces, they either lack privacy guarantees and/or result in an unacceptable trade-off between privacy and utility. To address this challenge, we propose Generative BlendPose-Latent Diffusion Model (LDM), a framework for face image anonymization that leverages k -anonymity privacy guarantees with the capabilities of latent diffusion models, to generate high-quality anonymous images. Our approach uses uniformly blended pose-guided and class-consistent k -anonymous images together with text prompts aimed at preserving non-identifying demographic features and guide the diffusion-based denoising. Comprehensive comparisons against related works –including plain k -anonymous and differentially private mechanisms and generative methods–, show that our method results in stronger anonymization, better utility preservation, and improved visual fidelity and realism.