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The rapid evolution of automatic facial indexing tech-nologies increases the risk of compromising personal and sensitive information. To address the issue we propose cre-ating cartoon avatars or'toon avatars' designed to effec-tively obscure identity features. The primary objective is to deceive current AI systems preventing them from accu-rately identifying individuals while making minimal modi-fications to their facial features. Moreover we aim to en-sure that a human observer can still recognize the person depicted in these altered avatar images. To achieve this we introduce'ToonerGAN'a novel approach that utilizes Generative Adversarial Networks (GANs) to craft person-alized cartoon avatars. The ToonerGAN framework con-sists of a style module and a de-identification module that work together to produce high-resolution realistic cartoon images. For the efficient training of our network we have developed an extensive dataset named'ToonSet'compris-ing approximately 23000 facial images and their cartoon renditions. Through comprehensive experiments and bench-marking against existing datasets including CelebA-HQ our method demonstrates superior performance in obfus-cating identity while preserving the utility of data. Addi-tionally a user-centric study to explore the effectiveness of ToonerGAN has yielded some compelling observations.