{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,23]],"date-time":"2025-08-23T20:40:02Z","timestamp":1755981602164,"version":"3.44.0"},"publisher-location":"New York, NY, USA","reference-count":69,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,12,5]],"date-time":"2023-12-05T00:00:00Z","timestamp":1701734400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"KAUST Baseline Research Scheme","award":["KAUST"],"award-info":[{"award-number":["KAUST"]}]},{"name":"SDAIA-KAUST Center of Excellence in Data Science and Artificial Intelligence","award":["KAUST"],"award-info":[{"award-number":["KAUST"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,12,8]]},"DOI":"10.1145\/3630048.3630182","type":"proceedings-article","created":{"date-parts":[[2023,11,28]],"date-time":"2023-11-28T19:42:35Z","timestamp":1701200555000},"page":"49-84","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Federated Learning is Better with Non-Homomorphic Encryption"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5986-0855","authenticated-orcid":false,"given":"Konstantin","family":"Burlachenko","sequence":"first","affiliation":[{"name":"KAUST, Thuwal, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7808-4764","authenticated-orcid":false,"given":"Abdulmajeed","family":"Alrowithi","sequence":"additional","affiliation":[{"name":"Saudi Data and AI Authority, Riyadh, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3158-2977","authenticated-orcid":false,"given":"Fahad Ali","family":"Albalawi","sequence":"additional","affiliation":[{"name":"Saudi Data and AI Authority, Riyadh, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4380-5848","authenticated-orcid":false,"given":"Peter","family":"Richt\u00e1rik","sequence":"additional","affiliation":[{"name":"KAUST, Thuwal, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,12,5]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"2014. PyCryptodome. https:\/\/pypi.org\/project\/pycryptodome\/. Accessed: 2023-05-10."},{"key":"e_1_3_2_1_2_1","volume-title":"12th USENIX symposium on operating systems design and implementation (OSDI 16)","author":"Abadi Mart\u00edn","year":"2016","unstructured":"Mart\u00edn Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, et al. 2016. {TensorFlow}: A System for {Large-Scale} Machine Learning. In 12th USENIX symposium on operating systems design and implementation (OSDI 16). 265--283."},{"key":"e_1_3_2_1_3_1","volume-title":"QSGD: Communication-efficient SGD via gradient quantization and encoding. Advances in neural information processing systems 30","author":"Alistarh Dan","year":"2017","unstructured":"Dan Alistarh, Demjan Grubic, Jerry Li, Ryota Tomioka, and Milan Vojnovic. 2017. QSGD: Communication-efficient SGD via gradient quantization and encoding. Advances in neural information processing systems 30 (2017)."},{"key":"e_1_3_2_1_4_1","volume-title":"The convergence of sparsified gradient methods. Advances in Neural Information Processing Systems 31","author":"Alistarh Dan","year":"2018","unstructured":"Dan Alistarh, Torsten Hoefler, Mikael Johansson, Nikola Konstantinov, Sarit Khirirat, and C\u00e9dric Renggli. 2018. The convergence of sparsified gradient methods. Advances in Neural Information Processing Systems 31 (2018)."},{"key":"e_1_3_2_1_5_1","first-page":"1333","article-title":"Privacy-preserving deep learning via additively homomorphic encryption","volume":"13","author":"Aono Yoshinori","year":"2017","unstructured":"Yoshinori Aono, Takuya Hayashi, Lihua Wang, Shiho Moriai, et al. 2017. Privacy-preserving deep learning via additively homomorphic encryption. IEEE Transactions on Information Forensics and Security 13, 5 (2017), 1333--1345.","journal-title":"IEEE Transactions on Information Forensics and Security"},{"key":"e_1_3_2_1_6_1","volume-title":"Automation & Test in Europe Conference & Exhibition (DATE). IEEE, 1527--1532","author":"Aydin Furkan","year":"2022","unstructured":"Furkan Aydin, Emre Karabulut, Seetal Potluri, Erdem Alkim, and Aydin Aysu. 2022. Reveal: Single-trace side-channel leakage of the seal homomorphic encryption library. In 2022 Design, Automation & Test in Europe Conference & Exhibition (DATE). IEEE, 1527--1532."},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-44647-8_1"},{"key":"e_1_3_2_1_8_1","volume-title":"The EAX Mode of Operation (A Two-Pass Authenticated Encryption Scheme Optimized for Simplicity and Efficiency) 3017","author":"Bellare M","year":"2004","unstructured":"M Bellare, P Rogaway, and DWagner. 2004. The EAX Mode of Operation (A Two-Pass Authenticated Encryption Scheme Optimized for Simplicity and Efficiency) 3017 (2004), 389--407, Fast Software Encryption. Lecture Notes in Computer Science (2004)."},{"key":"e_1_3_2_1_9_1","volume-title":"Tenseal: A library for encrypted tensor operations using homomorphic encryption. arXiv preprint arXiv:2104.03152","author":"Benaissa Ayoub","year":"2021","unstructured":"Ayoub Benaissa, Bilal Retiat, Bogdan Cebere, and Alaa Eddine Belfedhal. 2021. Tenseal: A library for encrypted tensor operations using homomorphic encryption. arXiv preprint arXiv:2104.03152 (2021)."},{"key":"e_1_3_2_1_10_1","volume-title":"The Salsa20 family of stream ciphers. New stream cipher designs: the eSTREAM finalists","author":"Bernstein Daniel J","year":"2008","unstructured":"Daniel J Bernstein. 2008. The Salsa20 family of stream ciphers. New stream cipher designs: the eSTREAM finalists (2008), 84--97."},{"key":"e_1_3_2_1_11_1","volume-title":"Protection against reconstruction and its applications in private federated learning. arXiv preprint arXiv:1812.00984","author":"Bhowmick Abhishek","year":"2018","unstructured":"Abhishek Bhowmick, John Duchi, Julien Freudiger, Gaurav Kapoor, and Ryan Rogers. 2018. Protection against reconstruction and its applications in private federated learning. arXiv preprint arXiv:1812.00984 (2018)."},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/792538.792543"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-25385-0_19"},{"key":"e_1_3_2_1_14_1","volume-title":"A graduate course in applied cryptography. Draft 0.5","author":"Boneh Dan","year":"2020","unstructured":"Dan Boneh and Victor Shoup. 2020. A graduate course in applied cryptography. Draft 0.5 (2020)."},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/3488659.3493775"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-70278-0_1"},{"key":"e_1_3_2_1_17_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 33 (2020), 13773--13782.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-70694-8_15"},{"key":"e_1_3_2_1_19_1","volume-title":"Linear program reconstruction in practice. arXiv preprint arXiv:1810.05692","author":"Cohen Aloni","year":"2018","unstructured":"Aloni Cohen and Kobbi Nissim. 2018. Linear program reconstruction in practice. arXiv preprint arXiv:1810.05692 (2018)."},{"key":"e_1_3_2_1_20_1","volume-title":"Intel SGX explained. Cryptology ePrint Archive","author":"Costan Victor","year":"2016","unstructured":"Victor Costan and Srinivas Devadas. 2016. Intel SGX explained. Cryptology ePrint Archive (2016)."},{"key":"e_1_3_2_1_21_1","volume-title":"AES proposal: Rijndael. NIST AES Proposal","author":"Daemen Joan","year":"1999","unstructured":"Joan Daemen and Vincent Rijmen. 1999. AES proposal: Rijndael. NIST AES Proposal (1999)."},{"key":"e_1_3_2_1_22_1","first-page":"137","article-title":"Reijndael: The advanced encryption standard","volume":"26","author":"Daemen Joan","year":"2001","unstructured":"Joan Daemen and Vincent Rijmen. 2001. Reijndael: The advanced encryption standard. Dr. Dobb's Journal: Software Tools for the Professional Programmer 26, 3 (2001), 137--139.","journal-title":"Dr. Dobb's Journal: Software Tools for the Professional Programmer"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/773153.773173"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1007\/11761679_29"},{"volume-title":"Theory of cryptography conference","author":"Dwork Cynthia","key":"e_1_3_2_1_25_1","unstructured":"Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith. 2006. Calibrating noise to sensitivity in private data analysis. In Theory of cryptography conference. Springer, 265--284."},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-45848-4_57"},{"key":"e_1_3_2_1_27_1","volume-title":"Somewhat practical fully homomorphic encryption. Cryptology ePrint Archive","author":"Fan Junfeng","year":"2012","unstructured":"Junfeng Fan and Frederik Vercauteren. 2012. Somewhat practical fully homomorphic encryption. Cryptology ePrint Archive (2012)."},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.3390\/cryptography6010013"},{"key":"e_1_3_2_1_29_1","unstructured":"Craig Gentry. 2009. A fully homomorphic encryption scheme. Stanford university."},{"key":"e_1_3_2_1_30_1","volume-title":"International Conference on Machine Learning. PMLR, 3788--3798","author":"Gorbunov Eduard","year":"2021","unstructured":"Eduard Gorbunov, Konstantin P Burlachenko, Zhize Li, and Peter Richt\u00e1rik. 2021. MARINA: Faster non-convex distributed learning with compression. In International Conference on Machine Learning. PMLR, 3788--3798."},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-52153-4_6"},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"crossref","unstructured":"K. He et al. 2016. Deep Residual Learning for Image Recognition. In CVPR. 770--778.","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1109\/ISSCC.2014.6757323"},{"key":"e_1_3_2_1_35_1","volume-title":"Marco Canini, and Peter Richt\u00e1rik.","author":"Horv\u00f3th Samuel","year":"2022","unstructured":"Samuel Horv\u00f3th, Chen-Yu Ho, Ludovit Horvath, Atal Narayan Sahu, Marco Canini, and Peter Richt\u00e1rik. 2022. Natural compression for distributed deep learning. In Mathematical and Scientific Machine Learning. PMLR, 129--141."},{"key":"e_1_3_2_1_36_1","volume-title":"Revisiting Fully Homomorphic Encryption Schemes. arXiv preprint arXiv:2305.05904","author":"Jain Nimish","year":"2023","unstructured":"Nimish Jain and Aswani Kumar Cherukuri. 2023. Revisiting Fully Homomorphic Encryption Schemes. arXiv preprint arXiv:2305.05904 (2023)."},{"key":"e_1_3_2_1_37_1","volume-title":"Quantum analysis of aes. Cryptology ePrint Archive","author":"Jang Kyungbae","year":"2022","unstructured":"Kyungbae Jang, Anubhab Baksi, Hyunji Kim, Gyeongju Song, Hwajeong Seo, and Anupam Chattopadhyay. 2022. Quantum analysis of aes. Cryptology ePrint Archive (2022)."},{"key":"e_1_3_2_1_38_1","first-page":"1","article-title":"Beyond Data and Model Parallelism for Deep Neural Networks","volume":"1","author":"Jia Zhihao","year":"2019","unstructured":"Zhihao Jia, Matei Zaharia, and Alex Aiken. 2019. Beyond Data and Model Parallelism for Deep Neural Networks. Proceedings of Machine Learning and Systems 1 (2019), 1--13.","journal-title":"Proceedings of Machine Learning and Systems"},{"key":"e_1_3_2_1_39_1","volume-title":"Flashe: Additively symmetric homomorphic encryption for cross-silo federated learning. arXiv preprint arXiv:2109.00675","author":"Jiang Zhifeng","year":"2021","unstructured":"Zhifeng Jiang, Wei Wang, and Yang Liu. 2021. Flashe: Additively symmetric homomorphic encryption for cross-silo federated learning. arXiv preprint arXiv:2109.00675 (2021)."},{"key":"e_1_3_2_1_40_1","volume-title":"Kallista Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, et al.","author":"Kairouz Peter","year":"2021","unstructured":"Peter Kairouz, H Brendan McMahan, Brendan Avent, Aur\u00e9lien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Kallista Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, et al. 2021. Advances and open problems in federated learning. Foundations and Trends\u00ae in Machine Learning 14, 1--2 (2021), 1--210."},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-020-0186-1"},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611973105.102"},{"key":"e_1_3_2_1_43_1","volume-title":"Hamid Reza Feyzmahdavian, and Mikael Johansson","author":"Khirirat Sarit","year":"2018","unstructured":"Sarit Khirirat, Hamid Reza Feyzmahdavian, and Mikael Johansson. 2018. Distributed learning with compressed gradients. arXiv preprint arXiv:1806.06573 (2018)."},{"key":"e_1_3_2_1_44_1","volume-title":"On Circuit Private, Multikey and Threshold Approximate Homomorphic Encryption. Cryptology ePrint Archive","author":"Kluczniak Kamil","year":"2023","unstructured":"Kamil Kluczniak and Giacomo Santato. 2023. On Circuit Private, Multikey and Threshold Approximate Homomorphic Encryption. Cryptology ePrint Archive (2023)."},{"key":"e_1_3_2_1_45_1","volume-title":"One weird trick for parallelizing convolutional neural networks. arXiv preprint arXiv:1404.5997","author":"Krizhevsky Alex","year":"2014","unstructured":"Alex Krizhevsky. 2014. One weird trick for parallelizing convolutional neural networks. arXiv preprint arXiv:1404.5997 (2014)."},{"key":"e_1_3_2_1_46_1","unstructured":"Alex Krizhevsky and Geoffrey Hinton. 2009. Learning multiple layers of features from tiny images. Technical Report 0. University of Toronto Toronto Ontario."},{"volume-title":"Protecting Privacy Through Homomorphic Encryption","author":"Lauter Kristin Estella","key":"e_1_3_2_1_47_1","unstructured":"Kristin Estella Lauter, Wei Dai, and Kim Laine. 2022. Protecting Privacy Through Homomorphic Encryption. Springer."},{"key":"e_1_3_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1109\/LCA.2019.2955119"},{"key":"e_1_3_2_1_49_1","unstructured":"Seppo Linnainmaa. 1970. The representation of the cumulative rounding error of an algorithm as a Taylor expansion of the local rounding errors. Ph.D. Dissertation. Master's Thesis (in Finnish) Univ. Helsinki."},{"key":"e_1_3_2_1_50_1","volume-title":"Secure model fusion for distributed learning using partial homomorphic encryption. Policy-Based Autonomic Data Governance","author":"Liu Changchang","year":"2019","unstructured":"Changchang Liu, Supriyo Chakraborty, and Dinesh Verma. 2019. Secure model fusion for distributed learning using partial homomorphic encryption. Policy-Based Autonomic Data Governance (2019), 154--179."},{"key":"e_1_3_2_1_51_1","first-page":"5925","article-title":"On privacy and personalization in cross-silo federated learning","volume":"35","author":"Liu Ken","year":"2022","unstructured":"Ken Liu, Shengyuan Hu, Steven Z Wu, and Virginia Smith. 2022. On privacy and personalization in cross-silo federated learning. Advances in Neural Information Processing Systems 35 (2022), 5925--5940.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.1145\/272991.272995"},{"key":"e_1_3_2_1_53_1","unstructured":"Brendan McMahan Eider Moore Daniel Ramage Seth Hampson and Blaise Aguera y Arcas. 2017. Communication-efficient learning of deep networks from decentralized data. In Artificial intelligence and statistics. PMLR 1273--1282."},{"key":"e_1_3_2_1_54_1","unstructured":"Microsoft. 2021. Microsoft SEAL. https:\/\/github.com\/microsoft\/SEAL. Version 4.1."},{"key":"e_1_3_2_1_55_1","volume-title":"Pytorch: An imperative style, high-performance deep learning library. Advances in neural information processing systems 32","author":"Paszke Adam","year":"2019","unstructured":"Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. 2019. Pytorch: An imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019)."},{"key":"e_1_3_2_1_56_1","doi-asserted-by":"publisher","DOI":"10.1145\/3291047"},{"key":"e_1_3_2_1_57_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCITECHN.2008.4802973"},{"key":"e_1_3_2_1_58_1","doi-asserted-by":"publisher","DOI":"10.1145\/1568318.1568324"},{"key":"e_1_3_2_1_59_1","first-page":"4384","article-title":"EF21: A new, simpler, theoretically better, and practically faster error feedback","volume":"34","author":"Richt\u00e1rik Peter","year":"2021","unstructured":"Peter Richt\u00e1rik, Igor Sokolov, and Ilyas Fatkhullin. 2021. EF21: A new, simpler, theoretically better, and practically faster error feedback. Advances in Neural Information Processing Systems 34 (2021), 4384--4396.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_60_1","unstructured":"Ronald L Rivest Len Adleman Michael L Dertouzos et al. 1978. On data banks and privacy homomorphisms. Foundations of secure computation 4 11 (1978) 169--180."},{"key":"e_1_3_2_1_61_1","volume-title":"Learning representations by back-propagating errors. nature 323, 6088","author":"Rumelhart David E","year":"1986","unstructured":"David E Rumelhart, Geoffrey E Hinton, and Ronald J Williams. 1986. Learning representations by back-propagating errors. nature 323, 6088 (1986), 533--536."},{"key":"e_1_3_2_1_62_1","unstructured":"Mher Safaryan and Peter Richt\u00e1rik. 2019. On stochastic sign descent methods. (2019)."},{"volume-title":"Ethernet: the definitive guide. ''O'Reilly Media","author":"Spurgeon Charles E","key":"e_1_3_2_1_63_1","unstructured":"Charles E Spurgeon. 2000. Ethernet: the definitive guide. ''O'Reilly Media, Inc.''."},{"key":"e_1_3_2_1_64_1","volume-title":"Permutation compressors for provably faster distributed nonconvex optimization. arXiv preprint arXiv:2110.03300","author":"Szlendak Rafa\u0142","year":"2021","unstructured":"Rafa\u0142 Szlendak, Alexander Tyurin, and Peter Richt\u00e1rik. 2021. Permutation compressors for provably faster distributed nonconvex optimization. arXiv preprint arXiv:2110.03300 (2021)."},{"key":"e_1_3_2_1_65_1","volume-title":"Gradient sparsification for communication-efficient distributed optimization. Advances in Neural Information Processing Systems 31","author":"Wangni Jianqiao","year":"2018","unstructured":"Jianqiao Wangni, Jialei Wang, Ji Liu, and Tong Zhang. 2018. Gradient sparsification for communication-efficient distributed optimization. Advances in Neural Information Processing Systems 31 (2018)."},{"key":"e_1_3_2_1_66_1","volume-title":"Terngrad: Ternary gradients to reduce communication in distributed deep learning. Advances in neural information processing systems 30","author":"Wen Wei","year":"2017","unstructured":"Wei Wen, Cong Xu, Feng Yan, Chunpeng Wu, Yandan Wang, Yiran Chen, and Hai Li. 2017. Terngrad: Ternary gradients to reduce communication in distributed deep learning. Advances in neural information processing systems 30 (2017)."},{"key":"e_1_3_2_1_67_1","volume-title":"Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC 2020).","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 Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC 2020)."},{"key":"e_1_3_2_1_68_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2018.10.024"},{"key":"e_1_3_2_1_69_1","volume-title":"Faster rates for compressed federated learning with client-variance reduction. arXiv preprint arXiv:2112.13097","author":"Zhao Haoyu","year":"2021","unstructured":"Haoyu Zhao, Konstantin Burlachenko, Zhize Li, and Peter Richt\u00e1rik. 2021. Faster rates for compressed federated learning with client-variance reduction. arXiv preprint arXiv:2112.13097 (2021)."}],"event":{"name":"CoNEXT 2023: The 19th International Conference on emerging Networking EXperiments and Technologies","sponsor":["SIGCOMM ACM Special Interest Group on Data Communication"],"location":"Paris France","acronym":"CoNEXT 2023"},"container-title":["Proceedings of the 4th International Workshop on Distributed Machine Learning"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3630048.3630182","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3630048.3630182","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,23]],"date-time":"2025-08-23T20:15:18Z","timestamp":1755980118000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3630048.3630182"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,5]]},"references-count":69,"alternative-id":["10.1145\/3630048.3630182","10.1145\/3630048"],"URL":"https:\/\/doi.org\/10.1145\/3630048.3630182","relation":{},"subject":[],"published":{"date-parts":[[2023,12,5]]},"assertion":[{"value":"2023-12-05","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}