Practical k-Anonymization Approach for Multiple Faces in Photographs

Recently, the increased utilization of data has led to the development of various services. However, using data necessitates anonymization techniques for privacy. multi-input k-anonymizer unit (MIKU) [1], a facial image anonymization method proposed by Nakamura et al., anonymizes images by averaging the latent vectors of facial images in the latent space of StyleGAN [2]. However, MIKU has two limitations: (1) it only targets single-face images, and (2) it merely calculates the centroid of the latent vectors between the original images, thus not preserving the statistical information of the original image dataset. Therefore, this paper proposes a framework to realize a k-anonymization method for photos containing multiple faces. The anonymization process uses a proposed framework comprising multiple modified artificial intelligence applications. First, identifiers, quasi-identifiers, and sensitive attributes are specified for a photo with multiple faces. This paper proposes three methods for generalizing age, one of the quasi-identifiers. The first is the random method, which replaces the age of the original image with the age of a random individual. The second is the uniform distribution method, which assigns ages based on the uniform distribution of age ranges after anonymization. The third is a kernel method that assigns ages using random numbers obtained from a probability density function computed by kernel density estimation. The experiments resulted in an Fréchet inception distance (FID) [2] equivalent to MIKU without any observed difference for the specified sensitive attributes. Furthermore, the most effective results were obtained using the kernel method, indicating that the proposed method is effective for the k-anonymization of facial images.