{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T06:00:28Z","timestamp":1772690428445,"version":"3.50.1"},"reference-count":46,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2024,5,31]],"date-time":"2024-05-31T00:00:00Z","timestamp":1717113600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Fundamental Research Funds for the Central Universities","award":["2020ZDPY0223"],"award-info":[{"award-number":["2020ZDPY0223"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>In light of growing concerns about the misuse of personal data resulting from the widespread use of artificial intelligence technology, it is necessary to implement robust privacy-protection methods. However, existing methods for protecting facial privacy suffer from issues such as poor visual quality, distortion and limited reusability. To tackle this challenge, we propose a novel approach called Diffusion Models for Face Privacy Protection (DIFP). Our method utilizes a face generator that is conditionally controlled and reality-guided to produce high-resolution encrypted faces that are photorealistic while preserving the naturalness and recoverability of the original facial information. We employ a two-stage training strategy to generate protected faces with guidance on identity and style, followed by an iterative technique for improving latent variables to enhance realism. Additionally, we introduce diffusion model denoising for identity recovery, which facilitates the removal of encryption and restoration of the original face when required. Experimental results demonstrate the effectiveness of our method in qualitative privacy protection, achieving high success rates in evading face-recognition tools and enabling near-perfect restoration of occluded faces.<\/jats:p>","DOI":"10.3390\/e26060479","type":"journal-article","created":{"date-parts":[[2024,5,31]],"date-time":"2024-05-31T06:35:32Z","timestamp":1717137332000},"page":"479","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Generation of Face Privacy-Protected Images Based on the Diffusion Model"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-6354-6107","authenticated-orcid":false,"given":"Xingyi","family":"You","sequence":"first","affiliation":[{"name":"National and Local Joint Engineering Laboratory of Internet Applied Technology on Mines, China University of Mining and Technology, Xuzhou 221008, China"},{"name":"School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221008, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6796-175X","authenticated-orcid":false,"given":"Xiaohu","family":"Zhao","sequence":"additional","affiliation":[{"name":"National and Local Joint Engineering Laboratory of Internet Applied Technology on Mines, China University of Mining and Technology, Xuzhou 221008, China"},{"name":"School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221008, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9623-4036","authenticated-orcid":false,"given":"Yue","family":"Wang","sequence":"additional","affiliation":[{"name":"National and Local Joint Engineering Laboratory of Internet Applied Technology on Mines, China University of Mining and Technology, Xuzhou 221008, China"},{"name":"School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221008, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weiqing","family":"Sun","sequence":"additional","affiliation":[{"name":"National and Local Joint Engineering Laboratory of Internet Applied Technology on Mines, China University of Mining and Technology, Xuzhou 221008, China"},{"name":"School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221008, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,5,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1016\/j.ins.2019.10.019","article-title":"Privacy-preserving clustering for big data in cyber-physical-social systems: Survey and perspectives","volume":"515","author":"Zhao","year":"2020","journal-title":"Inf. Sci."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"420","DOI":"10.1016\/j.ins.2019.05.053","article-title":"Efficient privacy preservation of big data for accurate data mining","volume":"527","author":"Chamikara","year":"2020","journal-title":"Inf. Sci."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3653297","article-title":"Privacy preservation of electronic health records in the modern era: A systematic survey","volume":"56","author":"Nowrozy","year":"2024","journal-title":"ACM Comput. Surv."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.jvcir.2024.104140","article-title":"Privacy-preserving face recognition method based on extensible feature extraction","volume":"100","author":"Hu","year":"2024","journal-title":"J. Vis. Commun. Image Represent."},{"key":"ref_5","first-page":"1","article-title":"Coordinate-wise monotonic transformations enable privacy-preserving age estimation with 3D face point cloud","volume":"10","author":"Yang","year":"2024","journal-title":"Sci. China Life Sci."},{"key":"ref_6","unstructured":"Xavier, M., and Michael, K. (2024, January 3\u20138). Who Wore It Best? And Who Paid Less? Effects of Privacy-Preserving Techniques Across Demographics. Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, Waikoloa, HI, USA."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"232","DOI":"10.1109\/TKDE.2005.32","article-title":"Preserving privacy by de-identifying face images","volume":"17","author":"Newton","year":"2005","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/1143518.1143519","article-title":"Blur filtration fails to preserve privacy for home-based video conferencing","volume":"13","author":"Neustaedter","year":"2006","journal-title":"ACM Trans.-Comput.-Hum. Interact. (TOCHI)"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1073","DOI":"10.1016\/j.ijhcs.2009.09.003","article-title":"Collocated photo sharing, story-telling, and the performance of self","volume":"67","author":"House","year":"2009","journal-title":"Int. J. Hum.-Comput. Stud."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2840","DOI":"10.1109\/TIFS.2024.3356233","article-title":"Securereid: Privacy-preserving anonymization for person re-identification","volume":"19","author":"Ye","year":"2024","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_11","first-page":"297","article-title":"Privacy-Preserving Photo Sharing on Online Social Networks: A Review","volume":"14","author":"Sajid","year":"2024","journal-title":"Int. J. Saf. Secur. Eng."},{"key":"ref_12","first-page":"101","article-title":"Exploring the future application of UAVs: Face image privacy protection scheme based on chaos and DNA cryptography","volume":"36","author":"Wen","year":"2024","journal-title":"J. King Saud Univ.-Comput. Inf. Sci."},{"key":"ref_13","first-page":"1","article-title":"Privacy-preserving Multi-biometric Indexing based on Frequent Binary Patterns","volume":"3","author":"Rathgeb","year":"2024","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"103566","DOI":"10.1016\/j.cose.2023.103566","article-title":"To pass or not to pass: Privacy-preserving physical access control","volume":"136","author":"Krenn","year":"2024","journal-title":"Comput. Secur."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"111338","DOI":"10.1016\/j.knosys.2023.111338","article-title":"A blockchain-based framework for federated learning with privacy preservation in power load forecasting","volume":"284","author":"Mao","year":"2024","journal-title":"Knowl.-Based Syst."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Yang, X., Dong, Y., Pang, T., Su, H., Zhu, J., Chen, Y., and Xue, H. (2021, January 10\u201317). Towards face encryption by generating adversarial identity masks. Proceedings of the IEEE\/CVF International Conference on Computer Vision, ICCV, Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.00387"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"601","DOI":"10.1177\/1359104518775154","article-title":"Is social media bad for mental health and wellbeing? exploring the perspectives of adolescents","volume":"23","author":"Dogra","year":"2018","journal-title":"Clin. Child Psychol. Psychiatry"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Su, J., Shukla, A., Goel, S., and Narayanan, A. (2017, January 3\u20137). De-anonymizing web browsing data with social networks. Proceedings of the 26th International Conference on World Wide Web, Perth, Australia.","DOI":"10.1145\/3038912.3052714"},{"key":"ref_19","unstructured":"Song, Y., and Ermon, S. (2019, January 8\u201314). Generative modeling by estimating gradients of the data distribution. Proceedings of the 33rd Conference on Neural Information Processing Systems (NeurIPS 2019), Vancouver, BC, Canada."},{"key":"ref_20","unstructured":"Song, Y., Sohl-Dickstein, J., Kingma, D.P., Kumar, A., Ermon, S., and Poole, B. (2020). Score-based generative modeling through stochastic differential equations. arXiv."},{"key":"ref_21","first-page":"6840","article-title":"Denoising diffusion probabilistic models","volume":"33","author":"Ho","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_22","first-page":"2256","article-title":"Deep unsupervised learning using nonequilibrium thermodynamics","volume":"23","author":"Weiss","year":"2015","journal-title":"Int. Conf. Mach. Learn."},{"key":"ref_23","unstructured":"Yang, L., Zhang, Z., Song, Y., Hong, S., Xu, R., Zhao, Y., Shao, Y., Zhang, W., Cui, B., and Yang, M.H. (2022). Diffusion models: A comprehensive survey of methods and applications. arXiv."},{"key":"ref_24","unstructured":"Hsu, H., Asoodeh, S., and Calmon, F. (2020, January 26\u201328). Obfuscation via information density estimation. Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS), Virtual."},{"key":"ref_25","unstructured":"de Freitas, J.M., and Geiger, B.C. (2022). Funck: Information funnels and bottlenecks for invariant representation learning. arXiv."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Huang, T.H., and Gamal, H.E. (2024). An efficient difference-of-convex solver for privacy funnel. arXiv.","DOI":"10.1109\/ISIT-W61686.2024.10591770"},{"key":"ref_27","unstructured":"Razeghi, B., Rahimi, P., and Marcel, S. (2024). Deep Privacy Funnel Model: From a Discriminative to a Generative Approach with an Application to Face Recognition. arXiv."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2158","DOI":"10.1109\/TPAMI.2020.3015420","article-title":"Sensitivenets: Learning agnostic representations with application to face images","volume":"43","author":"Morales","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Tran, L., Yin, X., and Liu, X. (2017, January 21\u201326). Disentangled representation learning gan for pose-invariant face recognition. Proceedings of the IEEE conference on computer vision and pattern recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.141"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Gong, S., Liu, X., and Jain, A.K. (2020, January 23\u201328). Jointly de-biasing face recognition and demographic attribute estimation. Proceedings of the Computer Vision\u2013ECCV 2020: 16th European Conference, Glasgow, UK. Part XXIX 16.","DOI":"10.1007\/978-3-030-58526-6_20"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Park, S., Hwang, S., Kim, D., and Byun, H. (2021, January 2\u20139). Learning disentangled representation for fair facial attribute classification via fairness-aware information alignment. Proceedings of the AAAI Conference on Artificial Intelligence, Virtual.","DOI":"10.1609\/aaai.v35i3.16341"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Li, Z., Hoogs, A., and Xu, C. (2022, January 23\u201327). Discover and mitigate unknown biases with debiasing alternate networks. Proceedings of the European Conference on Computer Vision, Tel Aviv, Israel.","DOI":"10.1007\/978-3-031-19778-9_16"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Suwa\u0142a, A., W\u00f3jcik, B., Proszewska, M., Tabor, J., Spurek, P., and \u015amieja, M. (2024, January 1\u20136). Face identity-aware disentanglement in stylegan. Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, Waikoloa, HI, USA.","DOI":"10.1109\/WACV57701.2024.00514"},{"key":"ref_34","unstructured":"Song, J., Meng, C., and Ermon, S. (2020). Denoising diffusion implicit models. arXiv."},{"key":"ref_35","unstructured":"Karras, T., Aila, T., Laine, S., and Lehtinen, J. (2017). Progressive growing of gans for improved quality, stability, and variation. arXiv."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Maximov, M., Elezi, I., and Leal-Taix\u00e9, L. (2020, January 13\u201319). CIAGAN: Conditional identity anonymization generative adversarial networks. Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition(CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00549"},{"key":"ref_37","unstructured":"Shan, S., Wenger, E., Zhang, J., Li, H., Zheng, H., and Zhao, Y.B. (2020, January 12\u201314). Fawkes: Protecting privacy against unauthorized deep learning models. Proceedings of the SEC\u201920: Proceedings of the 29th USENIX Conference on Security Symposium, Berkeley, CA, USA."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Hukkel\u00e5s, H., Mester, R., and Lindseth, F. (2019). DeepPrivacy: A generative adversarial network for face anonymization. arXiv.","DOI":"10.1007\/978-3-030-33720-9_44"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"You, Z., Li, S., Qian, Z., and Zhang, X. (2021, January 5\u20139). Reversible privacy-preserving recognition. Proceedings of the IEEE International Conference on Multimedia and Expo (ICME), Shenzhen, China.","DOI":"10.1109\/ICME51207.2021.9428115"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"566","DOI":"10.1016\/j.ins.2023.02.013","article-title":"Invertible mask network for face privacy preservation","volume":"629","author":"Yang","year":"2023","journal-title":"Inf. Sci."},{"key":"ref_41","unstructured":"Li, D., Wang, W., Zhao, K., Dong, J., and Tan, T. (2023, January 18\u201322). Riddle: Reversible and diversified de-identification with latent encryptor. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver, BC, Canada."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Gu, X., Luo, W., Ryoo, M.S., and Lee, Y.J. (2020, January 23\u201328). Password-conditioned anonymization and deanonymization with face identity transformers. Proceedings of the European Conference on Computer Vision (ECCV), Glasgow, UK.","DOI":"10.1007\/978-3-030-58592-1_43"},{"key":"ref_43","unstructured":"He, X., Zhu, M., Chen, D., Wang, N., and Gao, X. (2023). Diff-Privacy: Diffusion-based Face Privacy Protection. arXiv."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Schroff, F., Kalenichenko, D., and Philbin, J. (2015, January 7\u201312). Facenet: A unified embedding for face recognition and clustering. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298682"},{"key":"ref_45","unstructured":"(2024, February 10). Baidu Intelligent Cloud. Available online: https:\/\/cloud.baidu.com\/product\/face."},{"key":"ref_46","first-page":"2579","article-title":"Visualizing data using t-sne","volume":"9","author":"Hinton","year":"2008","journal-title":"J. Mach. Learn. Res."}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/26\/6\/479\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:51:39Z","timestamp":1760107899000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/26\/6\/479"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,31]]},"references-count":46,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2024,6]]}},"alternative-id":["e26060479"],"URL":"https:\/\/doi.org\/10.3390\/e26060479","relation":{},"ISSN":["1099-4300"],"issn-type":[{"value":"1099-4300","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,5,31]]}}}