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For a data holder, such as a hospital or a government entity, who has aprivately held collection of personal data, in which the revealing and/orprocessing of the personal identifiable data is restricted and prohibited bylaw. Then, "how can we ensure the data holder does conceal the identity of eachindividual in the imagery of personal data while still preserving certainuseful aspects of the data after de-identification?" becomes a challenge issue.In this work, we propose an approach towards high-resolution facial imagede-identification, called k-Same-Siamese-GAN, which leverages thek-Same-Anonymity mechanism, the Generative Adversarial Network, and thehyperparameter tuning methods. Moreover, to speed up model training and reducememory consumption, the mixed precision training technique is also applied tomake kSS-GAN provide guarantees regarding privacy protection on close-formidentities and be trained much more efficiently as well. Finally, to validateits applicability, the proposed work has been applied to actual datasets - RafDand CelebA for performance testing. Besides protecting privacy ofhigh-resolution facial images, the proposed system is also justified for itsability in automating parameter tuning and breaking through the limitation ofthe number of adjustable parameters.

k-Same-Siamese-GAN: k-Same Algorithm with Generative Adversarial Network  for Facial Image De-identification with Hyperparameter Tuning and Mixed  Precision Training