{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,17]],"date-time":"2025-09-17T04:38:19Z","timestamp":1758083899934,"version":"3.44.0"},"publisher-location":"Cham","reference-count":67,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783032046260"},{"type":"electronic","value":"9783032046277"}],"license":[{"start":{"date-parts":[[2025,9,16]],"date-time":"2025-09-16T00:00:00Z","timestamp":1757980800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,16]],"date-time":"2025-09-16T00:00:00Z","timestamp":1757980800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-3-032-04627-7_6","type":"book-chapter","created":{"date-parts":[[2025,9,16]],"date-time":"2025-09-16T02:06:34Z","timestamp":1757988394000},"page":"96-119","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["DP-DocLDM: Differentially Private Document Image Generation Using Latent Diffusion Models"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3098-2458","authenticated-orcid":false,"given":"Saifullah","family":"Saifullah","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9697-4285","authenticated-orcid":false,"given":"Stefan","family":"Agne","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6100-8255","authenticated-orcid":false,"given":"Andreas","family":"Dengel","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4239-6520","authenticated-orcid":false,"given":"Sheraz","family":"Ahmed","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,9,16]]},"reference":[{"key":"6_CR1","doi-asserted-by":"publisher","unstructured":"Abadi, M., et al.: Deep learning with differential privacy. In: Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security. ACM (2016). https:\/\/doi.org\/10.1145\/2976749.2978318","DOI":"10.1145\/2976749.2978318"},{"key":"6_CR2","doi-asserted-by":"publisher","unstructured":"Afzal, M.Z., Kolsch, A., Ahmed, S., Liwicki, M.: Cutting the error by half: investigation of very deep CNN and advanced training strategies for document image classification. In: Proceedings of the International Conference on Document Analysis and Recognition, ICDAR, vol. 1, pp. 883\u2013888 (2017). https:\/\/doi.org\/10.1109\/ICDAR.2017.149","DOI":"10.1109\/ICDAR.2017.149"},{"key":"6_CR3","doi-asserted-by":"crossref","unstructured":"Al-Rubaie, M., Chang, J.M.: Privacy-preserving machine learning: threats and solutions. IEEE Secur. Priv. 17(2), 49\u201358 (2019)","DOI":"10.1109\/MSEC.2018.2888775"},{"key":"6_CR4","unstructured":"Andrew, G., Thakkar, O., McMahan, B., Ramaswamy, S.: Differentially private learning with adaptive clipping. In: Ranzato, M., Beygelzimer, A., Dauphin, Y., Liang, P., Vaughan, J.W. (eds.) Advances in Neural Information Processing Systems, vol.\u00a034, pp. 17455\u201317466. Curran Associates, Inc. (2021). https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2021\/file\/91cff01af640a24e7f9f7a5ab407889f-Paper.pdf"},{"key":"6_CR5","doi-asserted-by":"crossref","unstructured":"Basu, P., Roy, T.S., Naidu, R., Muftuoglu, Z.: Privacy enabled financial text classification using differential privacy and federated learning. In: Proceedings of the 3rd Workshop on Economics and Natural Language Processing, ECONLP 2021, Stroudsburg, PA, USA, pp. 50\u201355. Association for Computational Linguistics (2021). https:\/\/aclanthology.org\/2021.econlp-1.7","DOI":"10.18653\/v1\/2021.econlp-1.7"},{"key":"6_CR6","unstructured":"Basu, P., Roy, T.S., Naidu, R., Muftuoglu, Z., Singh, S., Mireshghallah, F.: Benchmarking differential privacy and federated learning for bert models (2022)"},{"key":"6_CR7","unstructured":"Carlini, N., Liu, C., Erlingsson, \u00da., Kos, J., Song, D.: The secret Sharer: evaluating and testing unintended memorization in neural networks. In: Proceedings of 28th USENIX Security Symposium, pp. 267\u2013284 (2019)"},{"key":"6_CR8","unstructured":"Carlini, N., et al.: Extracting training data from large language models. In: 30th USENIX Security Symposium (USENIX Security 2021), pp. 2633\u20132650. USENIX Association (2021). https:\/\/www.usenix.org\/conference\/usenixsecurity21\/presentation\/carlini-extracting"},{"key":"6_CR9","unstructured":"Chen, D., Orekondy, T., Fritz, M.: GS-WGAN: a gradient-sanitized approach for learning differentially private generators. In: Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M., Lin, H. (eds.) Advances in Neural Information Processing Systems, vol.\u00a033, pp. 12673\u201312684. Curran Associates, Inc. (2020). https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2020\/file\/9547ad6b651e2087bac67651aa92cd0d-Paper.pdf"},{"key":"6_CR10","doi-asserted-by":"publisher","unstructured":"Coavoux, M., Narayan, S., Cohen, S.B.: Privacy-preserving neural representations of text. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, EMNLP 2018, pp. 1\u201310 (2020). https:\/\/doi.org\/10.18653\/v1\/d18-1001","DOI":"10.18653\/v1\/d18-1001"},{"key":"6_CR11","unstructured":"Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), Minneapolis, Minnesota, pp. 4171\u20134186. Association for Computational Linguistics (2019). https:\/\/aclanthology.org\/N19-1423"},{"key":"6_CR12","unstructured":"Dhariwal, P., Nichol, A.: Diffusion models beat GANs on image synthesis (2021). https:\/\/arxiv.org\/abs\/2105.05233"},{"key":"6_CR13","unstructured":"Dockhorn, T., Cao, T., Vahdat, A., Kreis, K.: Differentially private diffusion models (2023). https:\/\/openreview.net\/forum?id=pX21pH4CsNB"},{"key":"6_CR14","unstructured":"Dosovitskiy, A., et al.: An image is worth 16x16 words: transformers for image recognition at scale. In: International Conference on Learning Representations (2021). https:\/\/openreview.net\/forum?id=YicbFdNTTy"},{"key":"6_CR15","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1561\/0400000042","volume":"9","author":"C Dwork","year":"2014","unstructured":"Dwork, C., Roth, A., Dwork, C., Roth, A.: The algorithmic foundations of differential privacy. Found. Trends R Theor. Comput. Sci. 9, 211\u2013407 (2014). https:\/\/doi.org\/10.1561\/0400000042","journal-title":"Found. Trends R Theor. Comput. Sci."},{"key":"6_CR16","doi-asserted-by":"publisher","unstructured":"Dwork, C.: Differential privacy. In: Automata, Languages and Programming, pp. 1\u201312. Springer, Heidelberg (2006). https:\/\/doi.org\/10.1007\/11787006_1","DOI":"10.1007\/11787006_1"},{"key":"6_CR17","unstructured":"European Parliament, Council of the European Union: Regulation (EU) 2016\/679 of the European Parliament and of the Council. https:\/\/data.europa.eu\/eli\/reg\/2016\/679\/oj"},{"key":"6_CR18","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"387","DOI":"10.1007\/978-3-030-50417-5_29","volume-title":"Computational Science \u2013 ICCS 2020","author":"J Ferrando","year":"2020","unstructured":"Ferrando, J., et al.: Improving accuracy and speeding up document image classification through parallel systems. In: Krzhizhanovskaya, V.V., et al. (eds.) ICCS 2020. LNCS, vol. 12138, pp. 387\u2013400. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-50417-5_29"},{"key":"6_CR19","doi-asserted-by":"crossref","unstructured":"Feyisetan, O., Diethe, T., Drake, T.: Leveraging hierarchical representations for preserving privacy and utility in text. In: Proceedings - IEEE International Conference on Data Mining, ICDM, vol. 2019-November, pp. 210\u2013219 (2019). http:\/\/arxiv.org\/abs\/1910.08917","DOI":"10.1109\/ICDM.2019.00031"},{"key":"6_CR20","doi-asserted-by":"crossref","unstructured":"Fredrikson, M., Jha, S., Ristenpart, T.: Model inversion attacks that exploit confidence information and basic countermeasures. In: Proceedings of ACM Conference on Computer and Communications Security, vol. 2015-October, pp. 1322\u20131333. ACM, New York (2015). http:\/\/dx.doi.org\/10.1145\/2810103.2813677","DOI":"10.1145\/2810103.2813677"},{"key":"6_CR21","unstructured":"Ghalebikesabi, S., et al.: Differentially private diffusion models generate useful synthetic images (2023). https:\/\/arxiv.org\/abs\/2302.13861"},{"key":"6_CR22","unstructured":"Guan, H., et al.: Idnet: a novel dataset for identity document analysis and fraud detection (2024). https:\/\/arxiv.org\/abs\/2408.01690"},{"key":"6_CR23","doi-asserted-by":"publisher","first-page":"368","DOI":"10.1007\/978-3-031-70549-6_22","volume-title":"Document Analysis and Recognition - ICDAR 2024","author":"SJH Hamdani","year":"2024","unstructured":"Hamdani, S.J.H., Saifullah, S., Agne, S., Dengel, A., Ahmed, S.: Latent diffusion for guided document table generation. In: Barney Smith, E.H., Liwicki, M., Peng, L. (eds.) Document Analysis and Recognition - ICDAR 2024, pp. 368\u2013383. Springer, Cham (2024). https:\/\/doi.org\/10.1007\/978-3-031-70549-6_22"},{"key":"6_CR24","unstructured":"Harder, F., Jalali, M., Sutherland, D.J., Park, M.: Pre-trained perceptual features improve differentially private image generation. Trans. Mach. Learn. Res. (2023). https:\/\/openreview.net\/forum?id=R6W7zkMz0P"},{"key":"6_CR25","doi-asserted-by":"crossref","unstructured":"Harley, A.W., Ufkes, A., Derpanis, K.G.: Evaluation of deep convolutional nets for document image classification and retrieval. In: International Conference on Document Analysis and Recognition (ICDAR) (2015)","DOI":"10.1109\/ICDAR.2015.7333910"},{"key":"6_CR26","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770\u2013778 (2015). https:\/\/api.semanticscholar.org\/CorpusID:206594692","DOI":"10.1109\/CVPR.2016.90"},{"key":"6_CR27","doi-asserted-by":"publisher","unstructured":"He, L., Lu, Y., Corring, J., Florencio, D., Zhang, C.: Diffusion-based document layout generation, pp. 361\u2013378. Springer, Cham (2023). https:\/\/doi.org\/10.1007\/978-3-031-41676-7_21","DOI":"10.1007\/978-3-031-41676-7_21"},{"key":"6_CR28","unstructured":"Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models (2020). https:\/\/arxiv.org\/abs\/2006.11239"},{"key":"6_CR29","unstructured":"Ho, J., Salimans, T.: Classifier-free diffusion guidance (2022). https:\/\/arxiv.org\/abs\/2207.12598"},{"key":"6_CR30","doi-asserted-by":"publisher","unstructured":"Hoory, S., et al.: Learning and evaluating a differentially private pre-trained language model. In: Findings of the Association for Computational Linguistics, Findings of ACL: EMNLP 2021, pp. 1178\u20131189 (2021). https:\/\/doi.org\/10.18653\/v1\/2021.privatenlp-1.3","DOI":"10.18653\/v1\/2021.privatenlp-1.3"},{"key":"6_CR31","unstructured":"Hu, L., Habernal, I., Shen, L., Wang, D.: Differentially private natural language models: recent advances and future directions. arXiv abs\/2301.09112 (2023). https:\/\/arxiv.org\/abs\/2301.09112"},{"key":"6_CR32","doi-asserted-by":"crossref","unstructured":"Huang, Y., Lv, T., Cui, L., Lu, Y., Wei, F.: LayoutLMv3: pre-training for document AI with unified text and image masking. In: Proceedings of the 30th ACM International Conference on Multimedia, pp. 4083\u20134091. ACM, New York (2022). https:\/\/dl.acm.org\/doi\/10.1145\/3503161.3548112","DOI":"10.1145\/3503161.3548112"},{"key":"6_CR33","unstructured":"Koskela, A., Tobaben, M., Honkela, A.: Individual privacy accounting with gaussian differential privacy. In: The Eleventh International Conference on Learning Representations (2023). https:\/\/openreview.net\/forum?id=JmC_Tld3v-f"},{"key":"6_CR34","doi-asserted-by":"crossref","unstructured":"Lecuyer, M., Atlidakis, V., Geambasu, R., Hsu, D., Jana, S.: Certified robustness to adversarial examples with differential privacy (2019). https:\/\/arxiv.org\/abs\/1802.03471","DOI":"10.1109\/SP.2019.00044"},{"key":"6_CR35","doi-asserted-by":"publisher","unstructured":"Lee, C.Y., et al.: FormNet: structural encoding beyond sequential modeling in form document information extraction. In: Proceedings of the Annual Meeting of the Association for Computational Linguistics, vol.\u00a01, pp. 3735\u20133754. Long Papers (2022). https:\/\/doi.org\/10.18653\/v1\/2022.acl-long.260","DOI":"10.18653\/v1\/2022.acl-long.260"},{"key":"6_CR36","doi-asserted-by":"publisher","unstructured":"Li, J., Xu, Y., Lv, T., Cui, L., Zhang, C., Wei, F.: DIT: self-supervised pre-training for document image transformer. In: Proceedings of the 30th ACM International Conference on Multimedia, MM 2022, pp. 3530\u20133539. Association for Computing Machinery, New York (2022). https:\/\/doi.org\/10.1145\/3503161.3547911","DOI":"10.1145\/3503161.3547911"},{"key":"6_CR37","unstructured":"Li, X., Tramer, F., Liang, P., Hashimoto, T.: Large language models can be strong differentially private learners. In: International Conference on Learning Representations (2022). https:\/\/openreview.net\/forum?id=bVuP3ltATMz"},{"key":"6_CR38","unstructured":"Liew, S.P., Takahashi, T., Ueno, M.: PEARL: data synthesis via private embeddings and adversarial reconstruction learning. In: International Conference on Learning Representations (2022). https:\/\/openreview.net\/forum?id=M6M8BEmd6dq"},{"key":"6_CR39","unstructured":"Liu, M.F., Lyu, S., Vinaroz, M., Park, M.: Differentially private latent diffusion models. Trans. Mach. Learn. Res. (2024). https:\/\/openreview.net\/forum?id=AkdQ266kHj"},{"key":"6_CR40","unstructured":"Liu, R., Bu, Z.: Towards hyperparameter-free optimization with differential privacy. In: The Thirteenth International Conference on Learning Representations (2025). https:\/\/openreview.net\/forum?id=2kGKsyhtvh"},{"key":"6_CR41","doi-asserted-by":"crossref","unstructured":"Liu, Z., Mao, H., Wu, C.Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2022)","DOI":"10.1109\/CVPR52688.2022.01167"},{"key":"6_CR42","unstructured":"McMahan, H.B., Ramage, D., Talwar, K., Zhang, L.: Learning differentially private recurrent language models. In: International Conference on Learning Representations (2017)"},{"key":"6_CR43","doi-asserted-by":"crossref","unstructured":"Mercier, D., Lucieri, A., Munir, M., Dengel, A., Ahmed, S.: Evaluating privacy-preserving machine learning in critical infrastructures: a case study on time-series classification. IEEE Trans. Ind. Inform. (2021)","DOI":"10.1109\/TII.2021.3124476"},{"key":"6_CR44","doi-asserted-by":"publisher","unstructured":"Mironov, I.: R\u00e9nyi differential privacy. In: 2017 IEEE 30th Computer Security Foundations Symposium (CSF). IEEE (2017). https:\/\/doi.org\/10.1109\/csf.2017.11","DOI":"10.1109\/csf.2017.11"},{"key":"6_CR45","unstructured":"Papernot, N., Chien, S., Song, S., Thakurta, A., Erlingsson, U.: Making the shoe fit: architectures, initializations, and tuning for learning with privacy (2020). https:\/\/openreview.net\/forum?id=rJg851rYwH"},{"key":"6_CR46","doi-asserted-by":"crossref","unstructured":"Plant, R., Gkatzia, D., Giuffrida, V.: CAPE: context-aware private embeddings for private language learning. In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, Online and Punta Cana, Dominican Republic, pp. 7970\u20137978. Association for Computational Linguistics (2021). https:\/\/aclanthology.org\/2021.emnlp-main.628","DOI":"10.18653\/v1\/2021.emnlp-main.628"},{"key":"6_CR47","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"732","DOI":"10.1007\/978-3-030-86331-9_47","volume-title":"Document Analysis and Recognition \u2013 ICDAR 2021","author":"R Powalski","year":"2021","unstructured":"Powalski, R., Borchmann, \u0141, Jurkiewicz, D., Dwojak, T., Pietruszka, M., Pa\u0142ka, G.: Going Full-TILT boogie on document understanding with text-image-layout transformer. In: Llad\u00f3s, J., Lopresti, D., Uchida, S. (eds.) ICDAR 2021. LNCS, vol. 12822, pp. 732\u2013747. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-86331-9_47"},{"key":"6_CR48","doi-asserted-by":"crossref","unstructured":"Rombach, R., Blattmann, A., Lorenz, D., Esser, P., Ommer, B.: High-resolution image synthesis with latent diffusion models (2021)","DOI":"10.1109\/CVPR52688.2022.01042"},{"key":"6_CR49","unstructured":"Sadat, S., Buhmann, J., Bradley, D., Hilliges, O., Weber, R.M.: Cads: unleashing the diversity of diffusion models through condition-annealed sampling (2024). https:\/\/arxiv.org\/abs\/2310.17347"},{"key":"6_CR50","doi-asserted-by":"crossref","unstructured":"Saifullah, S., Agne, S., Dengel, A., Ahmed, S.: Docxclassifier: towards an interpretable deep convolutional neural network for document image classification (2022). https:\/\/doi.org\/10.36227\/techrxiv.19310489.v4","DOI":"10.36227\/techrxiv.19310489.v4"},{"key":"6_CR51","unstructured":"Saifullah, S., Agne, S., Dengel, A., Ahmed, S.: Pried-kie: towards privacy preserved document key information extraction (2023)"},{"key":"6_CR52","doi-asserted-by":"publisher","unstructured":"Saifullah, S., Mercier, D., Agne, S., Dengel, A., Ahmed, S.: Towards privacy preserved document image classification: a comprehensive benchmark. Int. J. Doc. Anal. Recognit. (IJDAR) 27(3), 475\u2013499 (2024). https:\/\/doi.org\/10.1007\/s10032-024-00469-8","DOI":"10.1007\/s10032-024-00469-8"},{"key":"6_CR53","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1007\/978-3-030-86549-8_9","volume-title":"Document Analysis and Recognition \u2013 ICDAR 2021","author":"Z Shen","year":"2021","unstructured":"Shen, Z., Zhang, R., Dell, M., Lee, B.C.G., Carlson, J., Li, W.: LayoutParser: a unified toolkit for deep learning based document image analysis. In: Llad\u00f3s, J., Lopresti, D., Uchida, S. (eds.) ICDAR 2021. LNCS, vol. 12821, pp. 131\u2013146. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-86549-8_9"},{"key":"6_CR54","doi-asserted-by":"publisher","unstructured":"Shokri, R., Stronati, M., Song, C., Shmatikov, V.: Membership inference attacks against machine learning models. In: Proceedings - IEEE Symposium on Security and Privacy, pp. 3\u201318 (2017). https:\/\/doi.org\/10.1109\/SP.2017.41","DOI":"10.1109\/SP.2017.41"},{"key":"6_CR55","doi-asserted-by":"crossref","unstructured":"Smith, R.: An overview of the tesseract OCR engine. In: ICDAR 2007: Proceedings of the Ninth International Conference on Document Analysis and Recognition, pp. 629\u2013633. IEEE Computer Society, Washington, DC, USA (2007). https:\/\/storage.googleapis.com\/pub-tools-public-publication-data\/pdf\/33418.pdf","DOI":"10.1109\/ICDAR.2007.4376991"},{"key":"6_CR56","unstructured":"Soboroff, I.: Complex document information processing (CDIP) dataset, national institute of standards and technology (2022)"},{"key":"6_CR57","unstructured":"Sohl-Dickstein, J., Weiss, E.A., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics (2015). https:\/\/arxiv.org\/abs\/1503.03585"},{"key":"6_CR58","doi-asserted-by":"publisher","unstructured":"Tanveer, N., Ul-Hasan, A., Shafait, F.: Diffusion models for document image generation. In: Document Analysis and Recognition - ICDAR 2023, Part III, pp. 438\u2013453. Springer, Heidelberg (2023). https:\/\/doi.org\/10.1007\/978-3-031-41682-8_27","DOI":"10.1007\/978-3-031-41682-8_27"},{"key":"6_CR59","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1007\/978-3-031-70552-6_12","volume-title":"Document Analysis and Recognition - ICDAR 2024","author":"R Tito","year":"2024","unstructured":"Tito, R., et al.: Privacy-aware document visual question answering. In: Barney Smith, E.H., Liwicki, M., Peng, L. (eds.) Document Analysis and Recognition - ICDAR 2024, pp. 199\u2013218. Springer, Cham (2024). https:\/\/doi.org\/10.1007\/978-3-031-70552-6_12"},{"key":"6_CR60","doi-asserted-by":"crossref","unstructured":"Torkzadehmahani, R., Kairouz, P., Paten, B.: DP-CGAN: differentially private synthetic data and label generation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (2019)","DOI":"10.1109\/CVPRW.2019.00018"},{"key":"6_CR61","unstructured":"Tsai, Y.L., et al.: Differentially private fine-tuning of diffusion models (2024). https:\/\/arxiv.org\/abs\/2406.01355"},{"key":"6_CR62","unstructured":"Wang, H., Pang, S., Lu, Z., Rao, Y., Zhou, Y., Xue, M.: DP-promise: differentially private diffusion probabilistic models for image synthesis. In: USENIX Security Symposium (2024). https:\/\/www.usenix.org\/conference\/usenixsecurity24\/presentation\/wang-haichen"},{"key":"6_CR63","doi-asserted-by":"crossref","unstructured":"Wunderlich, D., Bernau, D., Ald\u00e0, F., Parra-Arnau, J., Strufe, T.: On the privacy\u2013utility trade-off in differentially private hierarchical text classification. Appl. Sci. 12(21) (2022). http:\/\/arxiv.org\/abs\/2103.02895","DOI":"10.3390\/app122111177"},{"key":"6_CR64","doi-asserted-by":"crossref","unstructured":"Yin, H., Mallya, A., Vahdat, A., Alvarez, J.M., Kautz, J., Molchanov, P.: See through gradients: image batch recovery via gradinversion. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 16337\u201316346 (2021)","DOI":"10.1109\/CVPR46437.2021.01607"},{"key":"6_CR65","doi-asserted-by":"crossref","unstructured":"Yun, S., Han, D., Oh, S.J., Chun, S., Choe, J., Yoo, Y.: Cutmix: regularization strategy to train strong classifiers with localizable features (2019). https:\/\/arxiv.org\/abs\/1905.04899","DOI":"10.1109\/ICCV.2019.00612"},{"key":"6_CR66","unstructured":"Zhang, H., Cisse, M., Dauphin, Y.N., Lopez-Paz, D.: mixup: beyond empirical risk minimization (2018). https:\/\/arxiv.org\/abs\/1710.09412"},{"key":"6_CR67","doi-asserted-by":"crossref","unstructured":"Ziller, A., Usynin, D., Braren, R., Makowski, M., Rueckert, D., Kaissis, G.: Medical imaging deep learning with differential privacy. Sci. Rep. 11(1) (2021). http:\/\/dx.doi.org\/10.1038\/s41598-021-93030-0","DOI":"10.1038\/s41598-021-93030-0"}],"container-title":["Lecture Notes in Computer Science","Document Analysis and Recognition \u2013 ICDAR 2025"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-04627-7_6","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,16]],"date-time":"2025-09-16T02:06:55Z","timestamp":1757988415000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-04627-7_6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,16]]},"ISBN":["9783032046260","9783032046277"],"references-count":67,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-04627-7_6","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2025,9,16]]},"assertion":[{"value":"16 September 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICDAR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Document Analysis and Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Wuhan","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 September 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icdar2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/iapr.org\/icdar2025","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}