Balancing Privacy and Performance: A Many-in-One Approach for Image Anonymization
Xuemei Jia, Jiawei Du, Hui Wei, Ruinian Xue, Zheng Wang, Hongyuan Zhu, Jun Chen
Abstract
The effective utilization of data through Deep Neural Networks (DNNs) has profoundly influenced various aspects of society. The growing demand for high-quality, particularly personalized, data has spurred research efforts to prevent data leakage and protect privacy in recent years. Early privacy-preserving methods primarily relied on instance-wise modifications, such as erasing or obfuscating essential features for de-identification. However, this approach highlights an inherent trade-off: minimal modification offers insufficient privacy protection, while excessive modification significantly degrades task performance. In this paper, we propose a novel Recombining for Obfuscation (FRO) approach to address this trade-off. Unlike existing methods that generate one anonymized instance by perturbing the original data on a one-to-one basis, our FRO approach generates an anonymized instance by reassembling mixed ID-related features from multiple original data sources on a many-in-one basis. Instead of introducing additional noise for de-identification, our approach leverages the existing non-polluted features from other instances to anonymize data. Extensive experiments on identity identification tasks demonstrate that FRO outperforms previous state-of-the-art methods, not only in utility performance but also in visual anonymization.
BibTeX
@article{Jia_Du_Wei_Xue_Wang_Zhu_Chen_2025, title={Balancing Privacy and Performance: A Many-in-One Approach for Image Anonymization}, volume={39}, url={https://ojs.aaai.org/index.php/AAAI/article/view/33936}, DOI={10.1609/aaai.v39i17.33936}, abstractNote={The effective utilization of data through Deep Neural Networks (DNNs) has profoundly influenced various aspects of society. The growing demand for high-quality, particularly personalized, data has spurred research efforts to prevent data leakage and protect privacy in recent years. Early privacy-preserving methods primarily relied on instance-wise modifications, such as erasing or obfuscating essential features for de-identification. However, this approach highlights an inherent trade-off: minimal modification offers insufficient privacy protection, while excessive modification significantly degrades task performance. In this paper, we propose a novel Recombining for Obfuscation (FRO) approach to address this trade-off. Unlike existing methods that generate one anonymized instance by perturbing the original data on a one-to-one basis, our FRO approach generates an anonymized instance by reassembling mixed ID-related features from multiple original data sources on a many-in-one basis. Instead of introducing additional noise for de-identification, our approach leverages the existing non-polluted features from other instances to anonymize data. Extensive experiments on identity identification tasks demonstrate that FRO outperforms previous state-of-the-art methods, not only in utility performance but also in visual anonymization.}, number={17}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Jia, Xuemei and Du, Jiawei and Wei, Hui and Xue, Ruinian and Wang, Zheng and Zhu, Hongyuan and Chen, Jun}, year={2025}, month={Apr.}, pages={17608-17616} }