{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T08:54:22Z","timestamp":1781600062667,"version":"3.54.5"},"publisher-location":"New York, NY, USA","reference-count":61,"publisher":"ACM","license":[{"start":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T00:00:00Z","timestamp":1781568000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2026,6,17]]},"DOI":"10.1145\/3785353.3815094","type":"proceedings-article","created":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T08:11:30Z","timestamp":1781597490000},"page":"92-103","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Zero-Overhead Integrity Protection for Encrypted Federated Learning via Deterministic Self-Blinding"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1131-4115","authenticated-orcid":false,"given":"Reda","family":"Bellafqira","sequence":"first","affiliation":[{"name":"IMT Atlantique, Brest, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-3617-7225","authenticated-orcid":false,"given":"Pierre","family":"Mahieux","sequence":"additional","affiliation":[{"name":"IMT Atlantique, Brest, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5643-0224","authenticated-orcid":false,"given":"Gouenou","family":"Coatrieux","sequence":"additional","affiliation":[{"name":"IMT Atlantique, Brest, France"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,6,16]]},"reference":[{"key":"e_1_3_3_2_2_2","doi-asserted-by":"crossref","first-page":"952","DOI":"10.1145\/3412841.3441970","volume-title":"Proceedings of the 36th Annual ACM Symposium on Applied Computing","author":"Abuadbba Alsharif","year":"2021","unstructured":"Alsharif Abuadbba, Hyoungshick Kim, and Surya Nepal. 2021. DeepiSign: invisible fragile watermark to protect the integrity and authenticity of CNN. In Proceedings of the 36th Annual ACM Symposium on Applied Computing. 952\u2013959."},{"key":"e_1_3_3_2_3_2","doi-asserted-by":"crossref","unstructured":"Gorjan Alagic Gorjan Alagic Daniel Apon David Cooper Quynh Dang Thinh Dang John Kelsey Jacob Lichtinger Yi-Kai Liu Carl Miller et\u00a0al. 2022. Status report on the third round of the NIST post-quantum cryptography standardization process. (2022).","DOI":"10.6028\/NIST.IR.8413-upd1"},{"key":"e_1_3_3_2_4_2","doi-asserted-by":"crossref","unstructured":"Yoshinori Aono Takuya Hayashi Lihua Wang and Shiho Moriai. 2017. Privacy-Preserving Deep Learning via Additively Homomorphic Encryption. IEEE Transactions on Information Forensics and Security 13 5 (2017) 1333\u20131345.","DOI":"10.1109\/TIFS.2017.2787987"},{"key":"e_1_3_3_2_5_2","doi-asserted-by":"crossref","unstructured":"Marco Arazzi Serena Nicolazzo and Antonino Nocera. 2025. A fully privacy-preserving solution for anomaly detection in iot using federated learning and homomorphic encryption. Information Systems Frontiers 27 1 (2025) 367\u2013390.","DOI":"10.1007\/s10796-023-10443-0"},{"key":"e_1_3_3_2_6_2","doi-asserted-by":"crossref","unstructured":"Rezak Aziz Soumya Banerjee Samia Bouzefrane and Thinh Le\u00a0Vinh. 2023. Exploring homomorphic encryption and differential privacy techniques towards secure federated learning paradigm. Future internet 15 9 (2023) 310.","DOI":"10.3390\/fi15090310"},{"key":"e_1_3_3_2_7_2","doi-asserted-by":"crossref","unstructured":"Li Bai Haibo Hu Qingqing Ye Haoyang Li Leixia Wang and Jianliang Xu. 2024. Membership inference attacks and defenses in federated learning: A survey. Comput. Surveys 57 4 (2024) 1\u201335.","DOI":"10.1145\/3704633"},{"key":"e_1_3_3_2_8_2","doi-asserted-by":"crossref","first-page":"49","DOI":"10.23919\/WONS60642.2024.10449550","volume-title":"2024 19th Wireless On-Demand Network Systems and Services Conference (WONS)","author":"Bellafqira Reda","year":"2024","unstructured":"Reda Bellafqira, Gouenou Coatrieux, Mohammed Lansari, and Jilo Chala. 2024. FedCAM-Identifying Malicious Models in Federated Learning Environments Conditionally to Their Activation Maps. In 2024 19th Wireless On-Demand Network Systems and Services Conference (WONS). IEEE, 49\u201356."},{"key":"e_1_3_3_2_9_2","unstructured":"Daniel\u00a0J Beutel Taner Topal Akhil Mathur Xinchi Qiu Javier Fernandez-Marques Yan Gao Lorenzo Sani Kwing\u00a0Hei Li Titouan Parcollet Pedro Porto\u00a0Buarque de Gusm\u00c3\u0122o et\u00a0al. 2020. Flower: A friendly federated learning research framework. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2007.14390 (2020)."},{"key":"e_1_3_3_2_10_2","doi-asserted-by":"crossref","unstructured":"Rosepreet\u00a0Kaur Bhogal and Ajmer Singh. 2026. Artificial intelligence and machine learning in healthcare: a comprehensive review. Bulletin of Electrical Engineering and Informatics 15 1 (2026) 338\u2013349.","DOI":"10.11591\/eei.v15i1.9815"},{"key":"e_1_3_3_2_11_2","first-page":"1175","volume-title":"Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security (CCS)","author":"Bonawitz Keith","year":"2017","unstructured":"Keith Bonawitz, Vladimir Ivanov, Ben Kreuter, Antonio Marcedone, Brendan McMahan, Sarvar Patel, Daniel Ramage, Aaron Segal, and Karn Seth. 2017. Practical Secure Aggregation for Privacy-Preserving Machine Learning. In Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security (CCS). 1175\u20131191."},{"key":"e_1_3_3_2_12_2","doi-asserted-by":"crossref","first-page":"2496","DOI":"10.1109\/EMBC.2016.7591237","volume-title":"2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)","author":"Bouslimi D.","year":"2016","unstructured":"D. Bouslimi, R. Bellafqira, and G. Coatrieux. 2016. Data hiding in homomorphically encrypted medical images for verifying their reliability in both encrypted and spatial domains. In 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, Orlando, FL, USA, 2496\u20132499."},{"key":"e_1_3_3_2_13_2","doi-asserted-by":"crossref","first-page":"1659","DOI":"10.1109\/SP40001.2021.00042","volume-title":"2021 IEEE Symposium on Security and Privacy (SP)","author":"Brendel Jacqueline","year":"2021","unstructured":"Jacqueline Brendel, Cas Cremers, Dennis Jackson, and Mang Zhao. 2021. The provable security of ed25519: theory and practice. In 2021 IEEE Symposium on Security and Privacy (SP). IEEE, 1659\u20131676."},{"key":"e_1_3_3_2_14_2","first-page":"409","volume-title":"International conference on the theory and application of cryptology and information security","author":"Cheon Jung\u00a0Hee","year":"2017","unstructured":"Jung\u00a0Hee Cheon, Andrey Kim, Miran Kim, and Yongsoo Song. 2017. Homomorphic encryption for arithmetic of approximate numbers. In International conference on the theory and application of cryptology and information security. 409\u2013437."},{"key":"e_1_3_3_2_15_2","doi-asserted-by":"crossref","first-page":"350","DOI":"10.1007\/3-540-45067-X_30","volume-title":"Australasian Conference on Information Security and Privacy","author":"Damg\u00e5rd Ivan","year":"2003","unstructured":"Ivan Damg\u00e5rd and Mads Jurik. 2003. A length-flexible threshold cryptosystem with applications. In Australasian Conference on Information Security and Privacy. Springer, 350\u2013364."},{"key":"e_1_3_3_2_16_2","first-page":"185","volume-title":"Proceedings of the 27th International Database Engineered Applications Symposium","author":"Dayal Saroj","year":"2023","unstructured":"Saroj Dayal, Dima Alhadidi, Ali Abbasi\u00a0Tadi, and Noman Mohammed. 2023. Comparative analysis of membership inference attacks in federated learning. In Proceedings of the 27th International Database Engineered Applications Symposium. 185\u2013192."},{"key":"e_1_3_3_2_17_2","doi-asserted-by":"crossref","unstructured":"Xiaoyu Deng. 2025. Homomorphic Encryption-Based Data Integrity Verification and Anti-Tampering Mechanism in Cloud Storage Environment. (2025).","DOI":"10.20944\/preprints202512.0870.v1"},{"key":"e_1_3_3_2_18_2","doi-asserted-by":"crossref","unstructured":"Danny Dolev and Andrew Yao. 1983. On the security of public key protocols. IEEE Transactions on information theory 29 2 (1983) 198\u2013208.","DOI":"10.1109\/TIT.1983.1056650"},{"key":"e_1_3_3_2_19_2","doi-asserted-by":"crossref","unstructured":"Haokun Fang and Quan Qian. 2021. Privacy preserving machine learning with homomorphic encryption and federated learning. Future Internet 13 4 (2021) 94.","DOI":"10.3390\/fi13040094"},{"key":"e_1_3_3_2_20_2","doi-asserted-by":"crossref","unstructured":"Muhammad Firdaus Harashta\u00a0Tatimma Larasati and Kyung Hyune-Rhee. 2025. Blockchain-based federated learning with homomorphic encryption for privacy-preserving healthcare data sharing. Internet of Things 31 (2025) 101579.","DOI":"10.1016\/j.iot.2025.101579"},{"key":"e_1_3_3_2_21_2","doi-asserted-by":"crossref","unstructured":"Zhipin Gu Jiangyong Shi and Yuexiang Yang. 2025. ANODYNE: Mitigating backdoor attacks in federated learning. Expert Systems with Applications 259 (2025) 125359.","DOI":"10.1016\/j.eswa.2024.125359"},{"key":"e_1_3_3_2_22_2","unstructured":"Pengxin Guo Runxi Wang Shuang Zeng Jinjing Zhu Haoning Jiang Yanran Wang Yuyin Zhou Feifei Wang Hui Xiong and Liangqiong Qu. 2025. Exploring the vulnerabilities of federated learning: A deep dive into gradient inversion attacks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2025)."},{"key":"e_1_3_3_2_23_2","doi-asserted-by":"crossref","unstructured":"Ali Hatamizadeh Hongxu Yin Pavlo Molchanov Andriy Myronenko Wenqi Li Prerna Dogra Andrew Feng Mona\u00a0G Flores Jan Kautz Daguang Xu et\u00a0al. 2023. Do gradient inversion attacks make federated learning unsafe? IEEE Transactions on Medical Imaging 42 7 (2023) 2044\u20132056.","DOI":"10.1109\/TMI.2023.3239391"},{"key":"e_1_3_3_2_24_2","doi-asserted-by":"crossref","unstructured":"Neveen\u00a0Mohammad Hijazi Moayad Aloqaily Mohsen Guizani Bassem Ouni and Fakhri Karray. 2023. Secure federated learning with fully homomorphic encryption for iot communications. IEEE Internet of Things Journal 11 3 (2023) 4289\u20134300.","DOI":"10.1109\/JIOT.2023.3302065"},{"key":"e_1_3_3_2_25_2","doi-asserted-by":"crossref","unstructured":"Hongsheng Hu Xuyun Zhang Zoran Salcic Lichao Sun Kim-Kwang\u00a0Raymond Choo and Gillian Dobbie. 2023. Source inference attacks: Beyond membership inference attacks in federated learning. IEEE Transactions on Dependable and Secure Computing 21 4 (2023) 3012\u20133029.","DOI":"10.1109\/TDSC.2023.3321565"},{"key":"e_1_3_3_2_26_2","doi-asserted-by":"crossref","unstructured":"Siquan Huang Yijiang Li Xingfu Yan Ying Gao Chong Chen Leyu Shi Biao Chen and Wing\u00a0WY Ng. 2025. Scope: On detecting constrained backdoor attacks in federated learning. IEEE Transactions on Information Forensics and Security 20 (2025) 3302\u20133315.","DOI":"10.1109\/TIFS.2025.3533899"},{"key":"e_1_3_3_2_27_2","unstructured":"Yangsibo Huang Samyak Gupta Zhao Song Kai Li and Sanjeev Arora. 2021. Evaluating gradient inversion attacks and defenses in federated learning. Advances in neural information processing systems 34 (2021) 7232\u20137241."},{"key":"e_1_3_3_2_28_2","doi-asserted-by":"crossref","unstructured":"Bianca Jansen\u00a0van Rensburg Pauline Puteaux William Puech and Jean-Pierre Pedeboy. 2023. 3D Object Watermarking from Data Hiding in the Homomorphic Encrypted Domain. ACM Transactions on Multimedia Computing Communications and Applications 19 5s (Oct. 2023) 1\u201320.","DOI":"10.1145\/3588573"},{"key":"e_1_3_3_2_29_2","doi-asserted-by":"crossref","unstructured":"KA Jeeva and VS Sheeba. 2022. Privacy preserving reversible watermarking in the encrypted domain through self-blinding. International Journal of Bioinformatics Research and Applications 18 1-2 (2022) 49\u201367.","DOI":"10.1504\/IJBRA.2022.121759"},{"key":"e_1_3_3_2_30_2","doi-asserted-by":"crossref","unstructured":"Don Johnson Alfred Menezes and Scott Vanstone. 2001. The elliptic curve digital signature algorithm (ECDSA). International journal of information security 1 1 (2001) 36\u201363.","DOI":"10.1007\/s102070100002"},{"key":"e_1_3_3_2_31_2","doi-asserted-by":"crossref","unstructured":"RS Kanakasabapathi and JE Judith. 2025. An intelligent hybrid encryption framework for cloud systems in cybernetics using ISSO and Paillier cryptosystem. International Journal of Machine Learning and Cybernetics 16 12 (2025) 10541\u201310567.","DOI":"10.1007\/s13042-025-02785-9"},{"key":"e_1_3_3_2_32_2","first-page":"5132","volume-title":"International conference on machine learning","author":"Karimireddy Sai\u00a0Praneeth","year":"2020","unstructured":"Sai\u00a0Praneeth Karimireddy, Satyen Kale, Mehryar Mohri, Sashank Reddi, Sebastian Stich, and Ananda\u00a0Theertha Suresh. 2020. Scaffold: Stochastic controlled averaging for federated learning. In International conference on machine learning. PMLR, 5132\u20135143."},{"key":"e_1_3_3_2_33_2","doi-asserted-by":"crossref","unstructured":"Li Li Shengxian Wang Shanqing Zhang Ting Luo and Ching-Chun Chang. 2020. Homomorphic Encryption-Based Robust Reversible Watermarking for 3D Model. Symmetry 12 3 (March 2020) 347.","DOI":"10.3390\/sym12030347"},{"key":"e_1_3_3_2_34_2","unstructured":"Tian Li Anit\u00a0Kumar Sahu Manzil Zaheer Maziar Sanjabi Ameet Talwalkar and Virginia Smith. 2020. Federated optimization in heterogeneous networks. Proceedings of Machine learning and systems 2 (2020) 429\u2013450."},{"key":"e_1_3_3_2_35_2","volume-title":"Guidelines for the selection, configuration, and use of transport layer security (TLS) implementations","author":"McKay Kerry","year":"2017","unstructured":"Kerry McKay and David Cooper. 2017. Guidelines for the selection, configuration, and use of transport layer security (TLS) implementations. Technical Report. National Institute of Standards and Technology."},{"key":"e_1_3_3_2_36_2","first-page":"1273","volume-title":"Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS)","author":"McMahan Brendan","year":"2017","unstructured":"Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Ag\u00fcera\u00a0y Arcas. 2017. Communication-Efficient Learning of Deep Networks from Decentralized Data. In Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS). 1273\u20131282."},{"key":"e_1_3_3_2_37_2","first-page":"691","volume-title":"IEEE Symposium on Security and Privacy (S&P)","author":"Melis Luca","year":"2019","unstructured":"Luca Melis, Congzheng Song, Emiliano De\u00a0Cristofaro, and Vitaly Shmatikov. 2019. Exploiting Unintended Feature Leakage in Collaborative Learning. In IEEE Symposium on Security and Privacy (S&P). 691\u2013706."},{"key":"e_1_3_3_2_38_2","doi-asserted-by":"crossref","first-page":"2013","DOI":"10.1109\/SP54263.2024.00008","volume-title":"2024 IEEE Symposium on Security and Privacy (SP)","author":"Naseri Mohammad","year":"2024","unstructured":"Mohammad Naseri, Yufei Han, and Emiliano De\u00a0Cristofaro. 2024. BadVFL: Backdoor attacks in vertical federated learning. In 2024 IEEE Symposium on Security and Privacy (SP). IEEE, 2013\u20132028."},{"key":"e_1_3_3_2_39_2","doi-asserted-by":"publisher","DOI":"10.6028\/NIST.SP.800-57pt1r5"},{"key":"e_1_3_3_2_40_2","unstructured":"NVIDIA Developer Blog. 2024. Security for Data Privacy in Federated Learning with CUDA-Accelerated Homomorphic Encryption in XGBoost. https:\/\/developer.nvidia.com\/blog\/security-for-data-privacy-in-federated-learning-with-cuda-accelerated-homomorphic-encryption-in-xgboost\/. Accessed: 2026-03-02."},{"key":"e_1_3_3_2_41_2","first-page":"223","volume-title":"International conference on the theory and applications of cryptographic techniques","author":"Paillier Pascal","year":"1999","unstructured":"Pascal Paillier. 1999. Public-key cryptosystems based on composite degree residuosity classes. In International conference on the theory and applications of cryptographic techniques. Springer, 223\u2013238."},{"key":"e_1_3_3_2_42_2","doi-asserted-by":"crossref","unstructured":"Maxime Pistono Reda Bellafqira and Gouenou Coatrieux. 2021. Cryptosystem conversion packing and matrix processing of homomorphically encrypted data: application to IoT devices. IEEE Access 9 (2021) 28302\u201328316.","DOI":"10.1109\/ACCESS.2021.3058849"},{"key":"e_1_3_3_2_43_2","doi-asserted-by":"crossref","unstructured":"Pauline Puteaux Manon Vialle and William Puech. 2020. Homomorphic encryption-based LSB substitution for high capacity data hiding in the encrypted domain. IEEE Access 8 (2020) 108655\u2013108663.","DOI":"10.1109\/ACCESS.2020.3001385"},{"key":"e_1_3_3_2_44_2","unstructured":"Tyler Reinmund Lars Kunze and Marina Jirotka. 2026. Sociotechnical Challenges of Machine Learning in Healthcare and Social Welfare. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2601.11417 (2026)."},{"key":"e_1_3_3_2_45_2","unstructured":"Holger\u00a0R Roth Yan Cheng Yuhong Wen Isaac Yang Ziyue Xu Yuan-Ting Hsieh Kristopher Kersten Ahmed Harouni Can Zhao Kevin Lu et\u00a0al. 2022. Nvidia flare: Federated learning from simulation to real-world. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2210.13291 (2022)."},{"key":"e_1_3_3_2_46_2","first-page":"1","volume-title":"2023 20th Annual International Conference on Privacy, Security and Trust (PST)","author":"S\u00e9bert Arnaud\u00a0Grivet","year":"2023","unstructured":"Arnaud\u00a0Grivet S\u00e9bert, Marina Checri, Oana Stan, Renaud Sirdey, and Cedric Gouy-Pailler. 2023. Combining homomorphic encryption and differential privacy in federated learning. In 2023 20th Annual International Conference on Privacy, Security and Trust (PST). IEEE, 1\u20137."},{"key":"e_1_3_3_2_47_2","doi-asserted-by":"crossref","unstructured":"Mohsin Shah Weiming Zhang Honggang Hu Hang Zhou and Toqeer Mahmood. 2018. Homomorphic Encryption-Based Reversible Data Hiding for 3D Mesh Models. Arabian Journal for Science and Engineering 43 12 (Dec. 2018) 8145\u20138157.","DOI":"10.1007\/s13369-018-3354-4"},{"key":"e_1_3_3_2_48_2","doi-asserted-by":"crossref","unstructured":"Chenghui Shi Shouling Ji Xudong Pan Xuhong Zhang Mi Zhang Min Yang Jun Zhou Jianwei Yin and Ting Wang. 2024. Towards practical backdoor attacks on federated learning systems. IEEE Transactions on Dependable and Secure Computing 21 6 (2024) 5431\u20135447.","DOI":"10.1109\/TDSC.2024.3376790"},{"key":"e_1_3_3_2_49_2","doi-asserted-by":"crossref","unstructured":"Avirneni\u00a0Veda Sri Mahesh\u00a0Kumar Morampudi Mallikarjun\u00a0Reddy Dorsala Sriramulu Bojjagani and Muhammad\u00a0Khurram Khan. 2026. Privacy-Preserving Federated Learning for Retinal Disease Diagnosis using Paillier Homomorphic Encryption with Multiple Encryption Keys. IEEE Access (2026).","DOI":"10.1109\/ACCESS.2026.3664444"},{"key":"e_1_3_3_2_50_2","doi-asserted-by":"crossref","unstructured":"Bianca\u00a0Jansen Van\u00a0Rensburg Pauline Puteaux William Puech and Jean-Pierre Pedeboy. 2023. 3D object watermarking from data hiding in the homomorphic encrypted domain. ACM Transactions on Multimedia Computing Communications and Applications 19 5s (2023) 1\u201320.","DOI":"10.1145\/3588573"},{"key":"e_1_3_3_2_51_2","doi-asserted-by":"crossref","unstructured":"Yichen Wan Youyang Qu Wei Ni Yong Xiang Longxiang Gao and Ekram Hossain. 2024. Data and model poisoning backdoor attacks on wireless federated learning and the defense mechanisms: A comprehensive survey. IEEE Communications Surveys & Tutorials 26 3 (2024) 1861\u20131897.","DOI":"10.1109\/COMST.2024.3361451"},{"key":"e_1_3_3_2_52_2","doi-asserted-by":"publisher","unstructured":"Bo Wang Hongtao Li Yina Guo and Jie Wang. 2023. PPFLHE: A privacy-preserving federated learning scheme with homomorphic encryption for healthcare data. Applied Soft Computing 146 (2023) 110677. 10.1016\/j.asoc.2023.110677","DOI":"10.1016\/j.asoc.2023.110677"},{"key":"e_1_3_3_2_53_2","doi-asserted-by":"crossref","unstructured":"Hao-Tian Wu Yiu-ming Cheung and Jiwu Huang. 2016. Reversible data hiding in Paillier cryptosystem. Journal of Visual Communication and Image Representation 40 (2016) 765\u2013771.","DOI":"10.1016\/j.jvcir.2016.08.021"},{"key":"e_1_3_3_2_54_2","doi-asserted-by":"crossref","unstructured":"Weibin Wu Jun Wang Yangpan Zhang Zhe Liu Lu Zhou and Xiaodong Lin. 2023. Vpip: Values packing in paillier for communication efficient oblivious linear computations. IEEE Transactions on Information Forensics and Security 18 (2023) 4214\u20134228.","DOI":"10.1109\/TIFS.2023.3290483"},{"key":"e_1_3_3_2_55_2","first-page":"1","volume-title":"Proceedings of the International Workshop on Secure and Efficient Federated Learning","author":"Xia Yue","year":"2025","unstructured":"Yue Xia, Maximilian Egger, Christoph Hofmeister, and Rawad Bitar. 2025. LoByITFL: Low communication secure and private federated learning. In Proceedings of the International Workshop on Secure and Efficient Federated Learning. 1\u20136."},{"key":"e_1_3_3_2_56_2","doi-asserted-by":"crossref","unstructured":"Qiang Yang Yang Liu Tianjian Chen and Yongxin Tong. 2019. Federated Machine Learning: Concept and Applications. ACM Transactions on Intelligent Systems and Technology 10 2 (2019) 1\u201319.","DOI":"10.1145\/3298981"},{"key":"e_1_3_3_2_57_2","first-page":"493","volume-title":"2020 USENIX Annual Technical Conference (USENIX ATC 20)","author":"Zhang Chengliang","year":"2020","unstructured":"Chengliang Zhang, Suyi Li, Junzhe Xia, Wei Wang, Feng Yan, and Yang Liu. 2020. BatchCrypt: Efficient Homomorphic Encryption for Cross-Silo Federated Learning. In 2020 USENIX Annual Technical Conference (USENIX ATC 20). 493\u2013506."},{"key":"e_1_3_3_2_58_2","unstructured":"Yi Zhang Kun Tian Yunfan Lu Fengxia Liu Cheng Li Zixian Gong Zhe Hu Jia Li and Qun Xu. 2025. Reparable threshold Paillier encryption scheme for federated learning. Soft Computing (2025) 1\u20136."},{"key":"e_1_3_3_2_59_2","doi-asserted-by":"crossref","unstructured":"Zhengyang Zhang Chunmei Ma Baogui Huang Guangshun Li Zhaofeng Niu and Defu Qiu. 2025. Secure federated learning based on multi-round critical parameters. Neurocomputing (2025) 132471.","DOI":"10.1016\/j.neucom.2025.132471"},{"key":"e_1_3_3_2_60_2","doi-asserted-by":"crossref","unstructured":"Bian Zhu and Ling Niu. 2025. A privacy-preserving federated learning scheme with homomorphic encryption and edge computing. Alexandria Engineering Journal 118 (2025) 11\u201320.","DOI":"10.1016\/j.aej.2024.12.070"},{"key":"e_1_3_3_2_61_2","unstructured":"Hangyu Zhu Rui Wang Yaochu Jin and Kaitai Liang. 2020. Distributed Additive Encryption and Quantization for Privacy Preserving Federated Deep Learning. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2011.12623 (2020)."},{"key":"e_1_3_3_2_62_2","volume-title":"Advances in Neural Information Processing Systems (NeurIPS)","author":"Zhu Ligeng","year":"2019","unstructured":"Ligeng Zhu, Zhijian Liu, and Song Han. 2019. Deep Leakage from Gradients. In Advances in Neural Information Processing Systems (NeurIPS)."}],"event":{"name":"IH&MMSec '26: ACM Workshop on Information Hiding and Multimedia Security","location":"Firenze Italy","acronym":"IH&MMSec '26","sponsor":["SIGMM ACM Special Interest Group on Multimedia"]},"container-title":["Proceedings of the 2026 ACM Workshop on Information Hiding and Multimedia Security"],"original-title":[],"deposited":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T08:16:18Z","timestamp":1781597778000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3785353.3815094"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,16]]},"references-count":61,"alternative-id":["10.1145\/3785353.3815094","10.1145\/3785353"],"URL":"https:\/\/doi.org\/10.1145\/3785353.3815094","relation":{},"subject":[],"published":{"date-parts":[[2026,6,16]]},"assertion":[{"value":"2026-06-16","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}