{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,9]],"date-time":"2025-12-09T19:20:35Z","timestamp":1765308035865,"version":"3.46.0"},"publisher-location":"New York, NY, USA","reference-count":61,"publisher":"ACM","funder":[{"name":"National Key Research & Develop Plan","award":["2023YFB4503600"],"award-info":[{"award-number":["2023YFB4503600"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U23A20299,U24B20144, 62172424, 62276270, 62322214, 62436010, 62441230"],"award-info":[{"award-number":["U23A20299,U24B20144, 62172424, 62276270, 62322214, 62436010, 62441230"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,10,27]]},"DOI":"10.1145\/3746027.3755037","type":"proceedings-article","created":{"date-parts":[[2025,10,25]],"date-time":"2025-10-25T05:47:42Z","timestamp":1761371262000},"page":"11357-11366","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Differentially Private Visual Learning with Public Subspace Augmented by Synthetic Data"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-9449-6382","authenticated-orcid":false,"given":"Haichao","family":"Sha","sequence":"first","affiliation":[{"name":"School of Information, Renmin University of China, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1436-3916","authenticated-orcid":false,"given":"Yuncheng","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Information, Renmin University of China, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0823-3760","authenticated-orcid":false,"given":"Ruixuan","family":"Liu","sequence":"additional","affiliation":[{"name":"Computer Science, Emory University, Atlanta, GA, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6424-8633","authenticated-orcid":false,"given":"Yang","family":"Cao","sequence":"additional","affiliation":[{"name":"Computer Science, Tokyo Institute of Technology, Tokyo, Japan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8132-9382","authenticated-orcid":false,"given":"Hong","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Information, Renmin University of China, Beijing, China"}]}],"member":"320","published-online":{"date-parts":[[2025,10,27]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/2976749.2978318"},{"key":"e_1_3_2_1_2_1","volume-title":"Federated learning and differential privacy for medical image analysis. Scientific reports","author":"Adnan Mohammed","year":"2022","unstructured":"Mohammed Adnan, Shivam Kalra, Jesse C Cresswell, Graham W Taylor, and Hamid R Tizhoosh. 2022. Federated learning and differential privacy for medical image analysis. Scientific reports, Vol. 12, 1 (2022), 1953."},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/3658644.3690194"},{"key":"e_1_3_2_1_4_1","volume-title":"Limits of private learning with access to public data. Advances in neural information processing systems","author":"Alon Noga","year":"2019","unstructured":"Noga Alon, Raef Bassily, and Shay Moran. 2019. Limits of private learning with access to public data. Advances in neural information processing systems, Vol. 32 (2019)."},{"key":"e_1_3_2_1_5_1","volume-title":"International Conference on Machine Learning. PMLR, 517-535","author":"Amid Ehsan","year":"2022","unstructured":"Ehsan Amid, Arun Ganesh, Rajiv Mathews, Swaroop Ramaswamy, Shuang Song, Thomas Steinke, Vinith M Suriyakumar, Om Thakkar, and Abhradeep Thakurta. 2022. Public data-assisted mirror descent for private model training. In International Conference on Machine Learning. PMLR, 517-535."},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW59228.2023.00091"},{"key":"e_1_3_2_1_7_1","volume-title":"International Conference on Machine Learning. PMLR, 695-703","author":"Bassily Raef","year":"2020","unstructured":"Raef Bassily, Albert Cheu, Shay Moran, Aleksandar Nikolov, Jonathan Ullman, and Steven Wu. 2020. Private query release assisted by public data. In International Conference on Machine Learning. PMLR, 695-703."},{"key":"e_1_3_2_1_8_1","volume-title":"Towards hyperparameter-free optimization with differential privacy. ICLR","author":"Bu Zhiqi","year":"2025","unstructured":"Zhiqi Bu and Ruixuan Liu. 2025. Towards hyperparameter-free optimization with differential privacy. ICLR (2025)."},{"key":"e_1_3_2_1_9_1","volume-title":"Automatic clipping: Differentially private deep learning made easier and stronger. NeurIPS","author":"Bu Zhiqi","year":"2022","unstructured":"Zhiqi Bu, Yu-Xiang Wang, Sheng Zha, and George Karypis. 2022. Automatic clipping: Differentially private deep learning made easier and stronger. NeurIPS (2022)."},{"key":"e_1_3_2_1_10_1","volume-title":"NeurIPS","volume":"36","author":"Bu Zhiqi","year":"2024","unstructured":"Zhiqi Bu, Yu-Xiang Wang, Sheng Zha, and George Karypis. 2024. Automatic clipping: Differentially private deep learning made easier and stronger. NeurIPS, Vol. 36 (2024)."},{"key":"e_1_3_2_1_11_1","first-page":"13773","article-title":"Understanding gradient clipping in private sgd: A geometric perspective","volume":"33","author":"Chen Xiangyi","year":"2020","unstructured":"Xiangyi Chen, Steven Z Wu, and Mingyi Hong. 2020. Understanding gradient clipping in private sgd: A geometric perspective. Advances in Neural Information Processing Systems, Vol. 33 (2020), 13773-13782.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_12_1","volume-title":"The Eleventh International Conference on Learning Representations, ICLR","author":"Chung Hyungjin","year":"2023","unstructured":"Hyungjin Chung, Jeongsol Kim, Michael T Mccann, Marc L Klasky, and Jong Chul Ye. 2023. Diffusion Posterior Sampling for General Noisy Inverse Problems. In The Eleventh International Conference on Learning Representations, ICLR 2023. The International Conference on Learning Representations."},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"crossref","unstructured":"Rachel Cummings Damien Desfontaines David Evans Roxana Geambasu Yangsibo Huang Matthew Jagielski Peter Kairouz Gautam Kamath Sewoong Oh Olga Ohrimenko et al. 2023. Advancing differential privacy: Where we are now and future directions for real-world deployment. arXiv preprint arXiv:2304.06929 (2023).","DOI":"10.1162\/99608f92.d3197524"},{"key":"e_1_3_2_1_14_1","volume-title":"International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=BJlrF24twB","author":"Dangel Felix","year":"2020","unstructured":"Felix Dangel, Frederik Kunstner, and Philipp Hennig. 2020. BackPACK: Packing more into Backprop. In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=BJlrF24twB"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"e_1_3_2_1_16_1","volume-title":"Towards Theoretical Understandings of Self-Consuming Generative Models. In International Conference on Machine Learning. PMLR, 14228-14255","author":"Fu Shi","year":"2024","unstructured":"Shi Fu, Sen Zhang, Yingjie Wang, Xinmei Tian, and Dacheng Tao. 2024. Towards Theoretical Understandings of Self-Consuming Generative Models. In International Conference on Machine Learning. PMLR, 14228-14255."},{"key":"e_1_3_2_1_17_1","volume-title":"Towards a Theoretical Understanding of Synthetic Data in LLM Post-Training: A Reverse-Bottleneck Perspective. arXiv preprint arXiv:2410.01720","author":"Gan Zeyu","year":"2024","unstructured":"Zeyu Gan and Yong Liu. 2024. Towards a Theoretical Understanding of Synthetic Data in LLM Post-Training: A Reverse-Bottleneck Perspective. arXiv preprint arXiv:2410.01720 (2024)."},{"key":"e_1_3_2_1_18_1","volume-title":"International Conference on Machine Learning. PMLR, 10611-10627","author":"Ganesh Arun","year":"2023","unstructured":"Arun Ganesh, Mahdi Haghifam, Milad Nasr, Sewoong Oh, Thomas Steinke, Om Thakkar, Abhradeep Guha Thakurta, and Lun Wang. 2023. Why is public pretraining necessary for private model training?. In International Conference on Machine Learning. PMLR, 10611-10627."},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00819"},{"key":"e_1_3_2_1_20_1","volume-title":"Choosing public datasets for private machine learning via gradient subspace distance. arXiv preprint arXiv:2303.01256","author":"Gu Xin","year":"2023","unstructured":"Xin Gu, Gautam Kamath, and Zhiwei Steven Wu. 2023. Choosing public datasets for private machine learning via gradient subspace distance. arXiv preprint arXiv:2303.01256 (2023)."},{"key":"e_1_3_2_1_21_1","volume-title":"Gradient descent happens in a tiny subspace. arXiv preprint arXiv:1812.04754","author":"Gur-Ari Guy","year":"2018","unstructured":"Guy Gur-Ari, Daniel A Roberts, and Ethan Dyer. 2018. Gradient descent happens in a tiny subspace. arXiv preprint arXiv:1812.04754 (2018)."},{"key":"e_1_3_2_1_22_1","volume-title":"Denoising diffusion probabilistic models. Advances in neural information processing systems","author":"Ho Jonathan","year":"2020","unstructured":"Jonathan Ho, Ajay Jain, and Pieter Abbeel. 2020. Denoising diffusion probabilistic models. Advances in neural information processing systems, Vol. 33 (2020), 6840-6851."},{"key":"e_1_3_2_1_23_1","first-page":"4828","article-title":"Protecting Label Distribution in Cross-Silo Federated Learning. In IEEE SP","author":"Jiang Yangfan","year":"2024","unstructured":"Yangfan Jiang, Xinjian Luo, Yuncheng Wu, Xiaokui Xiao, and Beng Chin Ooi. 2024. Protecting Label Distribution in Cross-Silo Federated Learning. In IEEE SP, . IEEE, 4828-4847.","journal-title":"IEEE"},{"key":"e_1_3_2_1_24_1","volume-title":"Conference on Learning Theory. PMLR, 2717-2746","author":"Kairouz Peter","year":"2021","unstructured":"Peter Kairouz, Monica Ribero Diaz, Keith Rush, and Abhradeep Thakurta. 2021. (Nearly) Dimension Independent Private ERM with AdaGrad Rates via Publicly Estimated Subspaces. In Conference on Learning Theory. PMLR, 2717-2746."},{"key":"e_1_3_2_1_25_1","volume-title":"Elucidating the design space of diffusion-based generative models. Advances in neural information processing systems","author":"Karras Tero","year":"2022","unstructured":"Tero Karras, Miika Aittala, Timo Aila, and Samuli Laine. 2022. Elucidating the design space of diffusion-based generative models. Advances in neural information processing systems, Vol. 35 (2022), 26565-26577."},{"key":"e_1_3_2_1_26_1","volume-title":"Refining Generative Process with Discriminator Guidance in Score-based Diffusion Models. In International Conference on Machine Learning. PMLR, 16567-16598","author":"Kim Dongjun","year":"2023","unstructured":"Dongjun Kim, Yeongmin Kim, Se Jung Kwon, Wanmo Kang, and Il-Chul Moon. 2023. Refining Generative Process with Discriminator Guidance in Score-based Diffusion Models. In International Conference on Machine Learning. PMLR, 16567-16598."},{"key":"e_1_3_2_1_27_1","first-page":"2097","article-title":"On the generalization properties of diffusion models","volume":"36","author":"Li Puheng","year":"2023","unstructured":"Puheng Li, Zhong Li, Huishuai Zhang, and Jiang Bian. 2023a. On the generalization properties of diffusion models. Advances in Neural Information Processing Systems, Vol. 36 (2023), 2097-2127.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_28_1","first-page":"13086","article-title":"Private adaptive optimization with side information","author":"Li Tian","year":"2022","unstructured":"Tian Li, Manzil Zaheer, Sashank Reddi, and Virginia Smith. 2022c. Private adaptive optimization with side information. In ICML. PMLR, 13086-13105.","journal-title":"ICML. PMLR"},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611976236.22"},{"key":"e_1_3_2_1_30_1","first-page":"28616","article-title":"When does differentially private learning not suffer in high dimensions","volume":"35","author":"Li Xuechen","year":"2022","unstructured":"Xuechen Li, Daogao Liu, Tatsunori B Hashimoto, Huseyin A Inan, Janardhan Kulkarni, Yin-Tat Lee, and Abhradeep Guha Thakurta. 2022a. When does differentially private learning not suffer in high dimensions? Advances in Neural Information Processing Systems, Vol. 35 (2022), 28616-28630.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_31_1","unstructured":"Xuechen Li Florian Tramer Percy Liang and Tatsunori Hashimoto. 2022b. Large Language Models Can Be Strong Differentially Private Learners. In ICLR."},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.emnlp-main.647"},{"key":"e_1_3_2_1_33_1","volume-title":"Think locally, act globally: Federated learning with local and global representations. arXiv preprint arXiv:2001.01523","author":"Liang Paul Pu","year":"2020","unstructured":"Paul Pu Liang, Terrance Liu, Liu Ziyin, Nicholas B Allen, Randy P Auerbach, David Brent, Ruslan Salakhutdinov, and Louis-Philippe Morency. 2020. Think locally, act globally: Federated learning with local and global representations. arXiv preprint arXiv:2001.01523 (2020)."},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1145\/3664647.3681713"},{"key":"e_1_3_2_1_35_1","first-page":"4539","article-title":"PrivateRec","author":"Liu Ruixuan","year":"2023","unstructured":"Ruixuan Liu, Yang Cao, Yanlin Wang, Lingjuan Lyu, Yun Chen, and Hong Chen. 2023a. PrivateRec: Differentially Private Model Training and Online Serving for Federated News Recommendation. In SIGKDD. 4539-4548.","journal-title":"In SIGKDD."},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i10.26400"},{"key":"e_1_3_2_1_37_1","unstructured":"Andrew Lowy Zeman Li Tianjian Huang and Meisam Razaviyayn. 2024a. Optimal Differentially Private Model Training with Public Data. In ICML."},{"key":"e_1_3_2_1_38_1","volume-title":"How to Make the Gradients Small Privately: Improved Rates for Differentially Private Non-Convex Optimization. ICML","author":"Lowy Andrew","year":"2024","unstructured":"Andrew Lowy, Jonathan Ullman, and Stephen J Wright. 2024b. How to Make the Gradients Small Privately: Improved Rates for Differentially Private Non-Convex Optimization. ICML (2024)."},{"key":"e_1_3_2_1_39_1","unstructured":"Xinjian Luo Xiaokui Xiao Yuncheng Wu Juncheng Liu and Beng Chin Ooi. [n.d.]. A Fusion-Denoising Attack on InstaHide with Data Augmentation. In AAAI. 1899-1907."},{"key":"e_1_3_2_1_40_1","unstructured":"Frank Mcsherry. 2004. Spectral methods for data analysis. Ph.D. Dissertation. University of Washington."},{"key":"e_1_3_2_1_41_1","first-page":"263","article-title":"R\u00e9nyi differential privacy. In 2017 IEEE 30th computer security foundations symposium (CSF)","author":"Mironov Ilya","year":"2017","unstructured":"Ilya Mironov. 2017. R\u00e9nyi differential privacy. In 2017 IEEE 30th computer security foundations symposium (CSF). IEEE, 263-275.","journal-title":"IEEE"},{"key":"e_1_3_2_1_42_1","volume-title":"International Conference on Machine Learning. PMLR, 25718-25732","author":"Nasr Milad","year":"2023","unstructured":"Milad Nasr, Saeed Mahloujifar, Xinyu Tang, Prateek Mittal, and Amir Houmansadr. 2023. Effectively using public data in privacy preserving machine learning. In International Conference on Machine Learning. PMLR, 25718-25732."},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.01163"},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01042"},{"key":"e_1_3_2_1_45_1","volume-title":"Clip body and tail separately: High probability guarantees for DPSGD with heavy tails. arXiv preprint arXiv:2405.17529","author":"Sha Haichao","year":"2024","unstructured":"Haichao Sha, Yang Cao, Yong Liu, Yuncheng Wu, Ruixuan Liu, and Hong Chen. 2024. Clip body and tail separately: High probability guarantees for DPSGD with heavy tails. arXiv preprint arXiv:2405.17529 (2024)."},{"key":"e_1_3_2_1_46_1","volume-title":"Sketching for First Order Method: Efficient Algorithm for Low-Bandwidth Channel and Vulnerability. arXiv preprint arXiv:2210.08371","author":"Song Zhao","year":"2022","unstructured":"Zhao Song, Yitan Wang, Zheng Yu, and Lichen Zhang. 2022. Sketching for First Order Method: Efficient Algorithm for Low-Bandwidth Channel and Vulnerability. arXiv preprint arXiv:2210.08371 (2022)."},{"volume-title":"Upper and lower bounds for stochastic processes","author":"Talagrand Michel","key":"e_1_3_2_1_47_1","unstructured":"Michel Talagrand. 2014. Upper and lower bounds for stochastic processes. Vol. 60. Springer."},{"key":"e_1_3_2_1_48_1","volume-title":"NeurIPS","volume":"36","author":"Tang Xinyu","year":"2024","unstructured":"Xinyu Tang, Ashwinee Panda, Vikash Sehwag, and Prateek Mittal. 2024. Differentially private image classification by learning priors from random processes. NeurIPS, Vol. 36 (2024)."},{"key":"e_1_3_2_1_49_1","unstructured":"Florian Tramer and Dan Boneh. 2021. Differentially Private Learning Needs Better Features (or Much More Data). In ICLR."},{"key":"e_1_3_2_1_50_1","volume-title":"International Conference on Machine Learning. PMLR, 36246-36263","author":"Wang Zekai","year":"2023","unstructured":"Zekai Wang, Tianyu Pang, Chao Du, Min Lin, Weiwei Liu, and Shuicheng Yan. 2023. Better diffusion models further improve adversarial training. In International Conference on Machine Learning. PMLR, 36246-36263."},{"key":"e_1_3_2_1_51_1","first-page":"4680","article-title":"The alignment property of SGD noise and how it helps select flat minima: A stability analysis","volume":"35","author":"Wu Lei","year":"2022","unstructured":"Lei Wu, Mingze Wang, and Weijie Su. 2022. The alignment property of SGD noise and how it helps select flat minima: A stability analysis. Advances in Neural Information Processing Systems, Vol. 35 (2022), 4680-4693.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i9.26242"},{"key":"e_1_3_2_1_53_1","first-page":"2170","article-title":"A theory to instruct differentially-private learning via clipping bias reduction","author":"Xiao Hanshen","year":"2023","unstructured":"Hanshen Xiao, Zihang Xiang, Di Wang, and Srinivas Devadas. 2023. A theory to instruct differentially-private learning via clipping bias reduction. In S&P. IEEE, 2170-2189.","journal-title":"S&P. IEEE"},{"key":"e_1_3_2_1_54_1","unstructured":"Lingxiao Yang Shutong Ding Yifan Cai Jingyi Yu Jingya Wang and Ye Shi. 2024. Guidance with Spherical Gaussian Constraint for Conditional Diffusion. In ICML."},{"key":"e_1_3_2_1_55_1","volume-title":"arXiv preprint arXiv:2206.13033","author":"Yang Xiaodong","year":"2022","unstructured":"Xiaodong Yang, Huishuai Zhang, Wei Chen, and Tie-Yan Liu. 2022. Normalized\/clipped sgd with perturbation for differentially private non-convex optimization. arXiv preprint arXiv:2206.13033 (2022)."},{"key":"e_1_3_2_1_56_1","first-page":"703","article-title":"Differentially private learning needs hidden state (or much faster convergence)","volume":"35","author":"Ye Jiayuan","year":"2022","unstructured":"Jiayuan Ye and Reza Shokri. 2022. Differentially private learning needs hidden state (or much faster convergence). Advances in Neural Information Processing Systems, Vol. 35 (2022), 703-715.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_57_1","unstructured":"Da Yu Huishuai Zhang Wei Chen and Tie-Yan Liu. 2021a. Do not Let Privacy Overbill Utility: Gradient Embedding Perturbation for Private Learning. In ICLR."},{"key":"e_1_3_2_1_58_1","first-page":"12208","article-title":"Large scale private learning via low-rank reparametrization","author":"Yu Da","year":"2021","unstructured":"Da Yu, Huishuai Zhang, Wei Chen, Jian Yin, and Tie-Yan Liu. 2021b. Large scale private learning via low-rank reparametrization. In ICML. PMLR, 12208-12218.","journal-title":"ICML. PMLR"},{"key":"e_1_3_2_1_59_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.02118"},{"key":"e_1_3_2_1_60_1","unstructured":"Yingxue Zhou Steven Wu and Arindam Banerjee. 2021. Bypassing the Ambient Dimension: Private SGD with Gradient Subspace Identification. In ICLR."},{"key":"e_1_3_2_1_61_1","volume-title":"Improving Differentially Private SGD via Randomly Sparsified Gradients. Transactions on Machine Learning Research","author":"Zhu Junyi","year":"2023","unstructured":"Junyi Zhu and Matthew B Blaschko. 2023. Improving Differentially Private SGD via Randomly Sparsified Gradients. Transactions on Machine Learning Research (2023)."}],"event":{"name":"MM '25: The 33rd ACM International Conference on Multimedia","sponsor":["SIGMM ACM Special Interest Group on Multimedia"],"location":"Dublin Ireland","acronym":"MM '25"},"container-title":["Proceedings of the 33rd ACM International Conference on Multimedia"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3746027.3755037","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,9]],"date-time":"2025-12-09T19:16:47Z","timestamp":1765307807000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3746027.3755037"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,27]]},"references-count":61,"alternative-id":["10.1145\/3746027.3755037","10.1145\/3746027"],"URL":"https:\/\/doi.org\/10.1145\/3746027.3755037","relation":{},"subject":[],"published":{"date-parts":[[2025,10,27]]},"assertion":[{"value":"2025-10-27","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}