{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T11:07:43Z","timestamp":1772104063199,"version":"3.50.1"},"reference-count":73,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T00:00:00Z","timestamp":1772064000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Cryptography"],"abstract":"<jats:p>Deepfake technology can produce highly realistic manipulated media which pose as significant cybersecurity threats, including fraud, misinformation, and privacy violations. This research proposes a deepfake prevention approach based on symmetric and asymmetric ciphers. Post-quantum asymmetric ciphers were utilized to perform digital signature operations, which offer essential security services, including integrity, authentication, and non-repudiation. Symmetric ciphers were also employed to provide confidentiality and authentication. Unlike classical ciphers that are vulnerable to quantum attacks, this study adopts quantum-resilient ciphers to offer long-term security. The proposed approach enables entities to digitally sign media content before public release on other platforms. End users can subsequently verify the authenticity of content using the public keys of the media creators. To identify the most efficient ciphers to perform cryptography operations required for deepfake prevention, the study explores the implementation of quantum-resilient symmetric and asymmetric ciphers standardized by NIST, including Dilithium, Falcon, SPHINCS+, and Ascon-80pq. Additionally, this research provides comprehensive comparisons between the various classical and post-quantum ciphers in both categories: symmetric and asymmetric. Experimental results revealed that Dilithium-5 and Falcon-512 algorithms outperform other post-quantum ciphers, with a time delay of 2.50 and 251 ms, respectively, for digital signature operations. The Falcon-512 algorithm also demonstrates superior resource efficiency, making it a cost-effective choice for digital signature operations. With respect to symmetric ciphers, Ascon-80pq achieved the lowest time consumption, taking just 0.015 ms to perform encryption and decryption operations. Also, it is a significant option for constrained devices, since it consumes fewer resources compared to standard symmetric ciphers, such as AES. Through comprehensive evaluations and comparisons of various symmetric and asymmetric ciphers, this study serves as a blueprint to identify the most efficient ciphers to perform the cryptography operations necessary for deepfake prevention.<\/jats:p>","DOI":"10.3390\/cryptography10020015","type":"journal-article","created":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T09:31:32Z","timestamp":1772098292000},"page":"15","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Performance Evaluation of NIST-Standardized Post-Quantum and Symmetric Ciphers for Mitigating Deepfakes"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9922-6645","authenticated-orcid":false,"given":"Mohammad","family":"Alkhatib","sequence":"first","affiliation":[{"name":"Department of Computer Science, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11623, Saudi Arabia"}]}],"member":"1968","published-online":{"date-parts":[[2026,2,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_2","unstructured":"Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014). Generative adversarial nets. Adv. Neural Inf. Process. Syst., 27."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3425780","article-title":"The creation and detection of deepfakes: A survey","volume":"54","author":"Mirsky","year":"2021","journal-title":"ACM Comput. Surv."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/j.bushor.2019.11.006","article-title":"Deepfakes: Trick or treat?","volume":"63","author":"Kietzmann","year":"2020","journal-title":"Bus. Horiz."},{"key":"ref_5","first-page":"147","article-title":"Deepfakes and the new disinformation war: The coming age of post-truth geopolitics","volume":"98","author":"Chesney","year":"2019","journal-title":"Foreign Aff."},{"key":"ref_6","unstructured":"Korshunov, P., and Marcel, S. (2018). Deepfakes: A new threat to face recognition? assessment and detection. arXiv."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"113368","DOI":"10.1016\/j.jbusres.2022.113368","article-title":"Deepfakes: Deceptions, mitigations, and opportunities","volume":"154","author":"Mustak","year":"2023","journal-title":"J. Bus. Res."},{"key":"ref_8","unstructured":"Miotti, A., and Wasil, A. (2024). Combatting deepfakes: Policies to address national security threats and rights violations. arXiv."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2641","DOI":"10.1007\/s43681-024-00542-2","article-title":"Decent deepfakes? Professional deepfake developers\u2019 ethical considerations and their governance potential","volume":"5","author":"Pawelec","year":"2024","journal-title":"AI Ethics"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"10-9734","DOI":"10.9734\/acri\/2024\/v24i12997","article-title":"Deepfake Regulations and Their Impact on Content Creation in the Entertainment Industry","volume":"24","author":"Fabuyi","year":"2024","journal-title":"Arch. Curr. Res. Int."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"58","DOI":"10.61796\/ejcblt.v1i9.1015","article-title":"Law Enforcement Against Deepfake Porn AI: Penegakan Hukum Terhadap Deepfake Porn AI","volume":"1","author":"Putra","year":"2024","journal-title":"Eur. J. Contemp. Bus. Law Technol. Cyber Law Blockchain Leg. Innov."},{"key":"ref_12","unstructured":"Mahashreshty Vishweshwar, S. (2023). Implications of Deepfake Technology on Individual Privacy and Security. [Master\u2019s Thesis, St. Cloud State University]."},{"key":"ref_13","first-page":"62","article-title":"Regulations on Detecting, Punishing, Preventing Deepfake Technologies Based Forgery","volume":"2","author":"Lingyun","year":"2024","journal-title":"Cent. Asian J. Acad. Res."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"106024","DOI":"10.1016\/j.clsr.2024.106024","article-title":"When non-consensual intimate deepfakes go viral: The insufficiency of the UK Online Safety Act","volume":"54","author":"Kira","year":"2024","journal-title":"Comput. Law Secur. Rev."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1080\/23738871.2020.1797135","article-title":"Deepfake news: AI-enabled disinformation as a multi-level public policy challenge","volume":"5","author":"Whyte","year":"2020","journal-title":"J. Cyber Policy"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1177\/1354856520923963","article-title":"Whose dystopia is it anyway? Deepfakes and social media regulation","volume":"27","author":"Oppenheim","year":"2021","journal-title":"Convergence"},{"key":"ref_17","first-page":"914","article-title":"Integrating Legal, Ethical, and Technological Strategies to Mitigate AI Deepfake Risks through Strategic Communication","volume":"11","author":"Esezoobo","year":"2023","journal-title":"Int. J. Sci. Res. Manag."},{"key":"ref_18","first-page":"1","article-title":"Regulating deep fakes in the Artificial Intelligence Act","volume":"2","year":"2023","journal-title":"Appl. Cybersecur. Internet Gov."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"4785","DOI":"10.1109\/TMM.2022.3182509","article-title":"Unsupervised learning-based framework for deepfake video detection","volume":"25","author":"Zhang","year":"2022","journal-title":"IEEE Trans. Multimed."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Gupta, G., Raja, K., Gupta, M., Jan, T., Whiteside, S.T., and Prasad, M. (2023). A comprehensive review of deepfake detection using advanced machine learning and fusion methods. Electronics, 13.","DOI":"10.3390\/electronics13010095"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"John, J., and Sherif, B.V. (2022). Comparative analysis on different deepfake detection methods and semi supervised gan architecture for deepfake detection. Proceedings of the 2022 Sixth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud)(I-SMAC), IEEE.","DOI":"10.1109\/I-SMAC55078.2022.9987265"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"19759","DOI":"10.1007\/s00521-024-10181-7","article-title":"Deepfake detection using convolutional vision transformers and convolutional neural networks","volume":"36","author":"Soudy","year":"2024","journal-title":"Neural Comput. Appl."},{"key":"ref_23","unstructured":"Croitoru, F.A., Hiji, A.I., Hondru, V., Ristea, N.C., Irofti, P., Popescu, M., Rusu, C., Ionescu, R.T., Khan, F.S., and Shah, M. (2024). Deepfake Media Generation and Detection in the Generative AI Era: A Survey and Outlook. arXiv."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2151","DOI":"10.32604\/iasc.2023.029653","article-title":"Detecting deepfake images using deep learning techniques and explainable AI methods","volume":"35","author":"Abir","year":"2023","journal-title":"Intell. Autom. Soft Comput."},{"key":"ref_25","unstructured":"Pinhasov, B., Lapid, R., Ohayon, R., Sipper, M., and Aperstein, Y. (2024). Xai-based detection of adversarial attacks on deepfake detectors. arXiv."},{"key":"ref_26","first-page":"48","article-title":"Enhancing Deepfake Content Detection Through Blockchain Technology","volume":"16","author":"Mastoi","year":"2025","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Rout, J., Mishra, M., Das, S., and Barik, R.C. (2025). Blockchain Based Multimedia Content Authentication Using Ethereum: Transformation Towards Decentralized Framework. Proceedings of the 2025 International Conference on Microwave, Optical, and Communication Engineering (ICMOCE), IEEE.","DOI":"10.1109\/ICMOCE64100.2025.11076986"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"41596","DOI":"10.1109\/ACCESS.2019.2905689","article-title":"Combating deepfake videos using blockchain and smart contracts","volume":"7","author":"Hasan","year":"2019","journal-title":"IEEE Access"},{"key":"ref_29","unstructured":"Qureshi, A., Meg\u00edas, D., and Kuribayashi, M. (2021). Detecting deepfake videos using digital watermarking. Proceedings of the 2021 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), IEEE."},{"key":"ref_30","unstructured":"Mehrdad, S., Sadasivan, V.S., Zarei, A., Mahdavifar, H., and Feizi, S. (2024). DREW: Towards Robust Data Provenance by Leveraging Error-Controlled Watermarking. arXiv."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1145\/359340.359342","article-title":"A method for obtaining digital signatures and public-key cryptosystems","volume":"21","author":"Rivest","year":"1978","journal-title":"Commun. ACM"},{"key":"ref_32","unstructured":"Menezes, A., van Oorschot, P., and Vanstone, S. (2021). Handbook of Applied Cryptography, CRC Press."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1007\/s102070100002","article-title":"The elliptic curve digital signature algorithm (ECDSA)","volume":"1","author":"Johnson","year":"2001","journal-title":"Int. J. Inf. Secur."},{"key":"ref_34","unstructured":"Stallings, W. (2022). Cryptography and Network Security: Principles and Practice, Pearson Education Limited."},{"key":"ref_35","unstructured":"NIST (2022). Post-Quantum Cryptography Standardization Announcements."},{"key":"ref_36","first-page":"404","article-title":"The AES-256 cryptosystem resists quantum attacks","volume":"8","author":"Rao","year":"2017","journal-title":"Int. J. Adv. Res. Comput. Sci."},{"key":"ref_37","unstructured":"Oh, Y., Jang, K., and Seo, H. (2026, February 23). Quantum Security Evaluation of ASCON. Cryptology ePrint Archive (2025). Available online: https:\/\/eprint.iacr.org\/2025\/260."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Tan, C., Zhao, Y., Wei, S., Gu, G., Liu, P., and Wei, Y. (2024, January 24). Frequency-aware deepfake detection: Improving generalizability through frequency space domain learning. Proceedings of the AAAI Conference on Artificial Intelligence, Vancouver, BC, Canada.","DOI":"10.1609\/aaai.v38i5.28310"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Shi, C., Chen, L., Wang, C., Zhou, X., and Qin, Z. (2023). Review of image forensic techniques based on deep learning. Mathematics, 11.","DOI":"10.20944\/preprints202306.1179.v1"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Mansoor, N., and Iliev, A.I. (2025). Explainable AI for DeepFake Detection. Appl. Sci., 15.","DOI":"10.3390\/app15020725"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"22678","DOI":"10.1109\/ACCESS.2022.3152029","article-title":"Convolutional neural network based on diverse Gabor filters for deepfake recognition","volume":"10","author":"Khalifa","year":"2022","journal-title":"IEEE Access"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Venkateswarulu, S., and Srinagesh, A. (2024). DeepExplain: Enhancing DeepFake Detection Through Transparent and Explainable AI model. Informatica, 48.","DOI":"10.31449\/inf.v48i8.5792"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Abdullah, S.M., Cheruvu, A., Kanchi, S., Chung, T., Gao, P., Jadliwala, M., and Viswanath, B. (2024). An analysis of recent advances in deepfake image detection in an evolving threat landscape. Proceedings of the 2024 IEEE Symposium on Security and Privacy (SP), IEEE.","DOI":"10.1109\/SP54263.2024.00194"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Stanciu, D.C., and Ionescu, B. (2024, January 10). Improving generalization in deepfake detection via augmentation with recurrent adversarial attacks. Proceedings of the 3rd ACM International Workshop on Multimedia AI against Disinformation, Phuket, Thailand.","DOI":"10.1145\/3643491.3660291"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"7422","DOI":"10.1038\/s41598-023-34629-3","article-title":"Deep fake detection and classification using error-level analysis and deep learning","volume":"13","author":"Rafique","year":"2023","journal-title":"Sci. Rep."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Chang, X., Wu, J., Yang, T., and Feng, G. (2020). Deepfake face image detection based on improved VGG convolutional neural network. Proceedings of the 2020 39th Chinese Control Conference (CCC), IEEE.","DOI":"10.23919\/CCC50068.2020.9189596"},{"key":"ref_47","unstructured":"Badale, A., Castelino, L., Darekar, C., and Gomes, J. (2018, January 27\u201330). Deep fake detection using neural networks. Proceedings of the 15th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), Auckland, New Zealand."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Al-Dulaimi, O.A., and Kurnaz, S. (2024). A hybrid CNN-LSTM approach for precision deepfake image detection based on transfer learning. Electronics, 13.","DOI":"10.3390\/electronics13091662"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1016\/j.image.2018.05.015","article-title":"A deep learning approach to patch-based image inpainting forensics","volume":"67","author":"Zhu","year":"2018","journal-title":"Signal Process. Image Commun."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Lai, Z., Li, J., Wang, C., Wu, J., and Jiang, D. (2024). LIDeepDet: Deepfake Detection via Image Decomposition and Advanced Lighting Information Analysis. Electronics, 13.","DOI":"10.3390\/electronics13224466"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Raza, A., Munir, K., and Almutairi, M. (2022). A novel deep learning approach for deepfake image detection. Appl. Sci., 12.","DOI":"10.3390\/app12199820"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Hsu, C.C., Zhuang, Y.X., and Lee, C.Y. (2020). Deep fake image detection based on pairwise learning. Appl. Sci., 10.","DOI":"10.3390\/app10010370"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Cao, J., Deng, J., Yin, X., Yan, S., and Li, Z. (2023, January 17). WPCA: Wavelet Packets with Channel Attention for Detecting Face Manipulation. Proceedings of the 2023 15th International Conference on Machine Learning and Computing, Zhuhai, China.","DOI":"10.1145\/3587716.3587763"},{"key":"ref_54","first-page":"5295","article-title":"A Deepfake Detection Algorithm Based on Fourier Transform of Biological Signal","volume":"79","author":"Ni","year":"2024","journal-title":"Comput. Mater. Contin."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"art00011","DOI":"10.2352\/ISSN.2470-1173.2020.4.MWSF-117","article-title":"A system for mitigating the problem of deepfake news videos using watermarking","volume":"32","author":"Alattar","year":"2020","journal-title":"Electron. Imaging"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Hasan, K., Karimian, N., and Tehranipoor, S. (2024). Combating Deepfakes: A Novel Hybrid Hardware-Software Approach. Proceedings of the 2024 Silicon Valley Cybersecurity Conference (SVCC), IEEE.","DOI":"10.1109\/SVCC61185.2024.10637357"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1007\/s13278-022-00941-5","article-title":"An implementation of fake news prevention by blockchain and entropy-based incentive mechanism","volume":"12","author":"Chen","year":"2022","journal-title":"Soc. Netw. Anal. Min."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Parlak, M., Altunel, N.F., Akka\u015f, U.A., and Arici, E.T. (2022). Tamper-proof evidence via blockchain for autonomous vehicle accident monitoring. Proceedings of the 2022 IEEE 1st Global Emerging Technology Blockchain Forum: Blockchain & Beyond (iGETblockchain), IEEE.","DOI":"10.1109\/iGETblockchain56591.2022.10087067"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1109\/MITP.2022.3172653","article-title":"Defakepro: Decentralized deepfake attacks detection using enf authentication","volume":"24","author":"Nagothu","year":"2022","journal-title":"IT Prof."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Ali, A., Jadoon, Y.K., Farid, Z., Ahmad, M., Abidi, N., Alzoubi, H.M., and Alzoubi, A.A. (2022). The threat of deep fake technology to trusted identity management. Proceedings of the 2022 international conference on cyber resilience (ICCR), IEEE.","DOI":"10.1109\/ICCR56254.2022.9995978"},{"key":"ref_61","unstructured":"Koli, R.Y. (2024). Deepfake Detection System by Integrating Deep Learning and Blockchain Technology. [Doctoral Dissertation, National College of Ireland]."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Chandrappa, K., and Shankar, K.K. (2023). Combating deep fakes by the power of artificial intelligence and block chain in healthcare applications. Unleashing the Potentials of Blockchain Technology for Healthcare Industries, Academic Press.","DOI":"10.1016\/B978-0-323-99481-1.00012-2"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Choi, N., and Kim, H. (2023). DDS: Deepfake detection system through collective intelligence and deep-learning model in blockchain environment. Appl. Sci., 13.","DOI":"10.3390\/app13042122"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Costales, J.A., Shiromani, S., and Devaraj, M. (2023). The impact of blockchain technology to protect image and video integrity from identity theft using deepfake analyzer. Proceedings of the 2023 International Conference on Innovative Data Communication Technologies and Application (ICIDCA), IEEE.","DOI":"10.1109\/ICIDCA56705.2023.10099668"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Anitha, S., Anitha, N., Ashok, N., Daranya, T., Nandhini, B., and Chandrasekaran, V. (2023). Detection of Deepfakes in Financial Transactions Using Algorand Blockchain Consensus Mechanism. Proceedings of the International Conference on Network Security and Blockchain Technology, Springer Nature.","DOI":"10.1007\/978-981-99-4433-0_15"},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Alanzi, H.M., and Alkhatib, M. (2025). Blockchain-Based Identity Management System Prototype for Enhanced Privacy and Security. Electronics, 14.","DOI":"10.3390\/electronics14132605"},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Alkhatib, M., Albalawi, T., and Saeed, F. (2024). Blockchain-Based Quality Assurance System for Academic Programs. Appl. Sci., 14.","DOI":"10.3390\/app14114868"},{"key":"ref_68","first-page":"100040","article-title":"A secure deepfake mitigation framework: Architecture, issues, challenges, and societal impact","volume":"2","author":"Wazid","year":"2024","journal-title":"Cyber Secur. Appl."},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Al-Dabbagh, R., Alkhatib, M., and Albalawi, T. (2025). Efficient Post-Quantum Cryptography Algorithms for Auto-Enrollment in Public Key Infrastructure. Electronics, 14.","DOI":"10.3390\/electronics14101980"},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Karras, T., Laine, S., and Aila, T. (2019, January 16\u201321). A Style-Based Generator Architecture for Generative Adversarial Networks. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00453"},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Karras, T., Laine, S., Aittala, M., Hellsten, J., Lehtinen, J., and Aila, T. (2020, January 16\u201318). Analyzing and Improving the Image Quality of StyleGAN. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00813"},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Wang, S.-Y., Wang, O., Zhang, R., Owens, A., and Efros, A.A. (2020, January 14\u201319). CNN-Generated Images Are Surprisingly Easy to Spot\u2026 for Now. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00872"},{"key":"ref_73","unstructured":"McMahan, H.B., Moore, E., Ramage, D., Hampson, S., and Arcas, B.A.Y. (2017, January 20\u201322). Communication-Efficient Learning of Deep Networks from Decentralized Data. Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, Fort Lauderdale, FL, USA."}],"container-title":["Cryptography"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2410-387X\/10\/2\/15\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T10:11:56Z","timestamp":1772100716000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2410-387X\/10\/2\/15"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,26]]},"references-count":73,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2026,4]]}},"alternative-id":["cryptography10020015"],"URL":"https:\/\/doi.org\/10.3390\/cryptography10020015","relation":{},"ISSN":["2410-387X"],"issn-type":[{"value":"2410-387X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,26]]}}}