{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T15:32:17Z","timestamp":1776785537454,"version":"3.51.2"},"reference-count":124,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2025,5,7]],"date-time":"2025-05-07T00:00:00Z","timestamp":1746576000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,5,7]],"date-time":"2025-05-07T00:00:00Z","timestamp":1746576000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Inf. Secur."],"published-print":{"date-parts":[[2025,6]]},"DOI":"10.1007\/s10207-025-01045-9","type":"journal-article","created":{"date-parts":[[2025,5,7]],"date-time":"2025-05-07T06:24:35Z","timestamp":1746599075000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Advancements and challenges in privacy-preserving split learning: experimental findings and future directions"],"prefix":"10.1007","volume":"24","author":[{"given":"Afnan","family":"Alhindi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Saad","family":"Al-Ahmadi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohamed Maher Ben","family":"Ismail","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,5,7]]},"reference":[{"key":"1045_CR1","doi-asserted-by":"publisher","first-page":"2970","DOI":"10.3390\/math9222970","volume":"9","author":"AM Rahmani","year":"2021","unstructured":"Rahmani, A.M., Yousefpoor, E., Yousefpoor, M.S., Mehmood, Z., Haider, A., Hosseinzadeh, M., Naqvi, R.A.: Machine learning (ML) in medicine: review, applications, and challenges. Mathematics 9, 2970 (2021). https:\/\/doi.org\/10.3390\/math9222970","journal-title":"Mathematics"},{"key":"1045_CR2","doi-asserted-by":"publisher","DOI":"10.59400\/jam.v1i2.134","author":"B Nandi","year":"2023","unstructured":"Nandi, B., Jana, S., Das, K.P.: Machine learning-based approaches for financial market prediction: a comprehensive review. J. Appl. Math. (2023). https:\/\/doi.org\/10.59400\/jam.v1i2.134","journal-title":"J. Appl. Math."},{"key":"1045_CR3","doi-asserted-by":"publisher","first-page":"05002","DOI":"10.1051\/e3sconf\/202338705002","volume":"387","author":"BV Manikandan","year":"2023","unstructured":"Manikandan, B.V., Nathan, T.R., Naresh, R., Prasad, P.: Traffic flow prediction for intelligent transportation system using machine learning. E3S Web Conf. 387, 05002 (2023). https:\/\/doi.org\/10.1051\/e3sconf\/202338705002","journal-title":"E3S Web Conf."},{"key":"1045_CR4","doi-asserted-by":"publisher","first-page":"8485","DOI":"10.1609\/aaai.v36i8.20825","volume":"36","author":"C Thapa","year":"2022","unstructured":"Thapa, C., Arachchige, P.C.M., Camtepe, S., Sun, L.: SplitFed: when federated learning meets split learning. Proc. AAAI Conf. Artif. Intell. 36, 8485\u20138493 (2022). https:\/\/doi.org\/10.1609\/aaai.v36i8.20825","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"1045_CR5","doi-asserted-by":"crossref","unstructured":"Liu, B., Ding, M., Shaham, S., Rahayu, W., Farokhi, F., Lin, Z.: When machine learning meets privacy: a survey and outlook. arXiv: arXiv:2011.11819 (2021)","DOI":"10.1145\/3436755"},{"key":"1045_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3460427","volume":"54","author":"X Yin","year":"2021","unstructured":"Yin, X., Zhu, Y., Hu, J.: A comprehensive survey of privacy-preserving federated learning: a taxonomy, review, and future directions. ACM Comput. Surv. 54, 1\u201336 (2021). https:\/\/doi.org\/10.1145\/3460427","journal-title":"ACM Comput. Surv."},{"key":"1045_CR7","doi-asserted-by":"crossref","unstructured":"Gupta, O., Raskar, R.: Distributed learning of deep neural network over multiple agents. arXiv: arXiv:1810.06060 (2018)","DOI":"10.1016\/j.jnca.2018.05.003"},{"key":"1045_CR8","unstructured":"Li, Z., Yan, C., Zhang, X., Gharibi, G., Yin, Z., Jiang, X., Malin, B.A.: Split learning for distributed collaborative training of deep learning models in health informatics. arXiv: arXiv:2308.11027 (2023)"},{"key":"1045_CR9","doi-asserted-by":"crossref","unstructured":"Ha, Y.J., Yoo, M., Park, S., Jung, S., Kim, J.: Secure aerial surveillance using split learning. In: 2021 Twelfth International Conference on Ubiquitous and Future Networks (ICUFN), pp. 434\u2013437. IEEE, Jeju Island, Korea (2021)","DOI":"10.1109\/ICUFN49451.2021.9528601"},{"key":"1045_CR10","doi-asserted-by":"crossref","unstructured":"Ayad, A., Renner, M., Schmeink, A.: Improving the communication and computation efficiency of split learning for IoT applications. In: 2021 IEEE Global Communications Conference (GLOBECOM), pp. 1\u20136. IEEE, Madrid, Spain (2021)","DOI":"10.1109\/GLOBECOM46510.2021.9685493"},{"key":"1045_CR11","doi-asserted-by":"crossref","unstructured":"Yao, J.: Split learning for image classification in internet of drones networks. In: 2023 IEEE 24th International Conference on High Performance Switching and Routing (HPSR), pp. 52\u201355. IEEE, Albuquerque, NM, USA (2023)","DOI":"10.1109\/HPSR57248.2023.10147979"},{"key":"1045_CR12","doi-asserted-by":"crossref","unstructured":"Pasquini, D., Ateniese, G., Bernaschi, M.: Unleashing the tiger: inference attacks on split learning. arXiv: arXiv:2012.02670 (2021)","DOI":"10.1145\/3460120.3485259"},{"key":"1045_CR13","unstructured":"Mireshghallah, F., Taram, M., Vepakomma, P., Singh, A., Raskar, R., Esmaeilzadeh, H.: Privacy in deep learning: a survey. arXiv: arXiv:2004.12254 (2020)"},{"key":"1045_CR14","doi-asserted-by":"crossref","unstructured":"Nguyen, K., Khan, T., Michalas, A.: Split without a leak: reducing privacy leakage in split learning. arXiv: arXiv:2308.15783 (2023)","DOI":"10.1007\/978-3-031-64954-7_17"},{"key":"1045_CR15","doi-asserted-by":"crossref","unstructured":"Khan, T., Nguyen, K., Michalas, A.: Love or hate? Share or split? Privacy-preserving training using split learning and homomorphic encryption. arXiv: arXiv:2301.08778 (2023)","DOI":"10.1109\/PST58708.2023.10320153"},{"key":"1045_CR16","doi-asserted-by":"crossref","unstructured":"Abadi, M., Chu, A., Goodfellow, I., McMahan, H.B., Mironov, I., Talwar, K., Zhang, L.: Deep learning with differential privacy. In: Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, pp. 308\u2013318. ACM, (2016)","DOI":"10.1145\/2976749.2978318"},{"key":"1045_CR17","doi-asserted-by":"crossref","unstructured":"Singh, A., Chopra, A., Garza, E., Zhang, E., Vepakomma, P., Sharma, V., Raskar, R.: DISCO: dynamic and invariant sensitive channel obfuscation for deep neural networks. In: 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 12120\u201312130. IEEE, Nashville, TN, USA (2021)","DOI":"10.1109\/CVPR46437.2021.01195"},{"key":"1045_CR18","doi-asserted-by":"publisher","first-page":"97495","DOI":"10.1109\/access.2022.3204037","volume":"10","author":"EU Soykan","year":"2022","unstructured":"Soykan, E.U., Karacay, L., Karakoc, F., Tomur, E.: A survey and guideline on privacy enhancing technologies for collaborative machine learning. IEEE Access 10, 97495\u201397519 (2022). https:\/\/doi.org\/10.1109\/access.2022.3204037","journal-title":"IEEE Access"},{"key":"1045_CR19","doi-asserted-by":"publisher","first-page":"5983","DOI":"10.3390\/s22165983","volume":"22","author":"Q Duan","year":"2022","unstructured":"Duan, Q., Hu, S., Deng, R., Lu, Z.: Combined federated and split learning in edge computing for ubiquitous intelligence in internet of things: state-of-the-art and future directions. Sensors 22, 5983 (2022). https:\/\/doi.org\/10.3390\/s22165983","journal-title":"Sensors"},{"key":"1045_CR20","doi-asserted-by":"publisher","first-page":"54188","DOI":"10.1109\/ACCESS.2023.3281832","volume":"11","author":"C Shiranthika","year":"2023","unstructured":"Shiranthika, C., Saeedi, P., Baji\u0107, I.V.: Decentralized learning in healthcare: a review of emerging techniques. IEEE Access 11, 54188\u201354209 (2023). https:\/\/doi.org\/10.1109\/ACCESS.2023.3281832","journal-title":"IEEE Access"},{"key":"1045_CR21","doi-asserted-by":"publisher","unstructured":"Pham, N.D., Chilamkurti, N.: Data leakage threats and protection in split learning: a survey. In: Proceedings of the 2023 International Conference on Intelligent Computing and Its Emerging Applications (ICEA \u201823), pp. 141\u2013147. Association for Computing Machinery, New York, NY, USA (2024). https:\/\/doi.org\/10.1145\/3659154.3659189","DOI":"10.1145\/3659154.3659189"},{"key":"1045_CR22","doi-asserted-by":"publisher","first-page":"614","DOI":"10.1016\/j.procs.2024.03.251","volume":"233","author":"M Kiruthika","year":"2024","unstructured":"Kiruthika, M., Kumar, A., Krishnasamy, L., Sarveshwaran, V.: Investigation on preserving privacy of electronic medical record using split learning. Procedia Comput. Sci. 233, 614\u2013622 (2024). https:\/\/doi.org\/10.1016\/j.procs.2024.03.251","journal-title":"Procedia Comput. Sci."},{"key":"1045_CR23","unstructured":"McMahan, H.B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.Y.: Communication-efficient learning of deep networks from decentralized data. arXiv: arXiv:1602.05629 (2023)"},{"key":"1045_CR24","doi-asserted-by":"crossref","unstructured":"Kottursamy, K., Bashir, A.K., Kose, U., Uthra, A., Madhivanan, V., Mathivanan, P.: An evaluation on the performance of privacy preserving split neural networks using EMNIST dataset. In: Kottursamy, K., Bashir, A.K., Kose, U., Uthra, A. (eds.) Deep Sciences for Computing and Communications, vol. 1719, in Communications in Computer and Information Science, vol. 1719, pp. 332\u2013344. Springer Nature Switzerland, Cham (2023)","DOI":"10.1007\/978-3-031-27622-4"},{"key":"1045_CR25","doi-asserted-by":"crossref","unstructured":"Khowaja, S.A., Khuwaja, P., Dev, K., Singh, K., Nkenyereye, L., Kilper, D.: ZETA: zero-trust attack framework with split learning for autonomous vehicles in 6G networks. In: 2024 IEEE Wireless Communications and Networking Conference (WCNC), pp. 1\u20136. IEEE, Dubai, United Arab Emirates (2024)","DOI":"10.1109\/WCNC57260.2024.10571158"},{"key":"1045_CR26","unstructured":"Iqbal, A., Gope, P., Sikdar, B.: Privacy-preserving collaborative split learning framework for smart grid load forecasting. arXiv: arXiv:2403.01438 (2024)"},{"key":"1045_CR27","doi-asserted-by":"crossref","unstructured":"Tran, N.-P., Dao, N.-N., Do, Q.-T., Nguyen, T.-V., Cho, S.: Privacy-preserving traffic flow prediction: a split learning approach. In: 2023 International Conference on Information Networking (ICOIN), pp. 248\u2013250. IEEE, Bangkok, Thailand (2023)","DOI":"10.1109\/ICOIN56518.2023.10048996"},{"key":"1045_CR28","doi-asserted-by":"crossref","unstructured":"Pham, N.D., Abuadbba, A., Gao, Y., Phan, K.T., Chilamkurti, N.: Binarizing split learning for data privacy enhancement and computation reduction. arXiv: arXiv:2206.0486 (2023)","DOI":"10.1109\/TIFS.2023.3274391"},{"key":"1045_CR29","doi-asserted-by":"crossref","unstructured":"Abuadbba, S., Kim, K., Kim, M., Thapa, C., Camtepe, S.A., Gao, Y., Kim, H., Nepal, S.: Can we use split learning on 1D CNN models for privacy preserving training? arXiv: arXiv:2003.12365 (2020)","DOI":"10.1145\/3320269.3384740"},{"key":"1045_CR30","doi-asserted-by":"crossref","unstructured":"Oh, S., Park, J., Vepakomma, P., Baek, S., Raskar, R., Bennis, M., Kim, S.-L.: LocFedMix-SL: localize, federate, and mix for improved scalability, convergence, and latency in split learning. In: Proceedings of the ACM Web Conference 2022, pp. 3347\u20133357. ACM, Virtual Event, Lyon France (2022)","DOI":"10.1145\/3485447.3512153"},{"key":"1045_CR31","unstructured":"Pham, N.D., Phan, T.K., Abuadbba, A., Gao, Y., Nguyen, D., Chilamkurti, N.: Split learning without local weight sharing to enhance client-side data privacy. arXiv: arXiv:2212.00250 (2023)"},{"key":"1045_CR32","doi-asserted-by":"crossref","unstructured":"Ryu, J., Won, D., Lee, Y.: A study of split learning model. In: 2022 16th International Conference on Ubiquitous Information Management and Communication (IMCOM), pp. 1\u20134. IEEE, Seoul, Korea (2022)","DOI":"10.1109\/IMCOM53663.2022.9721798"},{"key":"1045_CR33","doi-asserted-by":"crossref","unstructured":"Jeon, J., Kim, J.: Privacy-sensitive parallel split learning. In: 2020 International Conference on Information Networking (ICOIN), pp. 7\u20139. IEEE, Barcelona, Spain (2020)","DOI":"10.1109\/ICOIN48656.2020.9016486"},{"key":"1045_CR34","doi-asserted-by":"crossref","unstructured":"Liu, Z., Guo, J., Yang, M., Yang, W., Fan, J., Lam, K.-Y.: Privacy-enhanced knowledge transfer with collaborative split learning over teacher ensembles. In: Proceedings of the 2023 Secure and Trustworthy Deep Learning Systems Workshop, pp. 1\u201313. ACM, Melbourne VIC Australia (2023)","DOI":"10.1145\/3591197.3591303"},{"key":"1045_CR35","unstructured":"Poirot, M.G., Vepakomma, P., Chang, K., Kalpathy-Cramer, J., Gupta, R., Raskar, R.: Split learning for collaborative deep learning in healthcare. arXiv: arXiv:1912.12115 (2019)"},{"key":"1045_CR36","doi-asserted-by":"crossref","unstructured":"Kim, J., Shin, S., Yu, Y., Lee, J., Lee, K.: Multiple classification with split learning. In: The 9th International Conference on Smart Media and Applications, pp. 358\u2013363. ACM, Jeju Republic of Korea (2020)","DOI":"10.1145\/3426020.3426131"},{"key":"1045_CR37","unstructured":"Rigaki, M., Garcia, S.: A survey of privacy attacks in machine learning. arXiv: arXiv:2007.07646 (2021)"},{"key":"1045_CR38","unstructured":"Qiu, X., Leontiadis, I., Melis, L., Sablayrolles, A., Stock, P.: EXACT: extensive attack for split learning. arXiv: arXiv:2305.12997 (2023)"},{"key":"1045_CR39","doi-asserted-by":"crossref","unstructured":"Erdogan, E., Kupcu, A., Cicek, A.E.: UnSplit: data-oblivious model inversion, model stealing, and label inference attacks against split learning. In: Proceedings of the 21st Workshop on Privacy in the Electronic Society, pp. 115\u2013124. ACM, (2022)","DOI":"10.1145\/3559613.3563201"},{"key":"1045_CR40","unstructured":"Liu, J., Lyu, X.: Clustering label inference attack against practical split learning. arXiv: arXiv:2203.05222 (2022)"},{"key":"1045_CR41","doi-asserted-by":"crossref","unstructured":"Shokri, R., Stronati, M., Song, C., Shmatikov, V.: Membership inference attacks against machine learning models. In: 2017 IEEE Symposium on Security and Privacy (SP), pp. 3\u201318. IEEE, San Jose, CA, USA (2017)","DOI":"10.1109\/SP.2017.41"},{"key":"1045_CR42","doi-asserted-by":"publisher","first-page":"477","DOI":"10.1109\/tii.2020.3046648","volume":"18","author":"H Chen","year":"2022","unstructured":"Chen, H., Li, H., Dong, G., Hao, M., Xu, G., Huang, X., Liu, Z.: Practical membership inference attack against collaborative inference in industrial IoT. IEEE Trans. Ind. Inf. 18, 477\u2013487 (2022). https:\/\/doi.org\/10.1109\/tii.2020.3046648","journal-title":"IEEE Trans. Ind. Inf."},{"key":"1045_CR43","doi-asserted-by":"crossref","unstructured":"He, Z., Zhang, T., Lee, R.B.: Model inversion attacks against collaborative inference. In: Proceedings of the 35th Annual Computer Security Applications Conference, pp. 148\u2013162. ACM, San Juan Puerto Rico USA (2019)","DOI":"10.1145\/3359789.3359824"},{"key":"1045_CR44","doi-asserted-by":"crossref","unstructured":"Fan, M., Chen, C., Wang, C., Zhou, W., Huang, J.: On the robustness of split learning against adversarial attacks. arXiv: arXiv:2307.07916 (2023)","DOI":"10.3233\/FAIA230330"},{"key":"1045_CR45","doi-asserted-by":"crossref","unstructured":"Jiang, L., Tan, R., Lou, X., Lin, G.: On lightweight privacy-preserving collaborative learning for internet-of-things objects. In: Proceedings of the International Conference on Internet of Things Design and Implementation, pp. 70\u201381. ACM, (2019)","DOI":"10.1145\/3302505.3310070"},{"key":"1045_CR46","doi-asserted-by":"crossref","unstructured":"Hesamifard, E., Takabi, H., Ghasemi, M.: Deep neural networks classification over encrypted data. arXiv: arXiv:1711.05189 (2019)","DOI":"10.1145\/3292006.3300044"},{"key":"1045_CR47","doi-asserted-by":"crossref","unstructured":"Osia, S.A., Taheri, A., Shamsabadi, A.S., Katevas, K., Haddadi, H., Rabiee, H.R.: Deep private-feature extraction. arXiv: arXiv:1802.03151 (2020)","DOI":"10.1109\/TKDE.2018.2878698"},{"key":"1045_CR48","doi-asserted-by":"crossref","unstructured":"Shokri, R., Shmatikov, V.: Privacy-preserving deep learning. In: Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security, pp. 1310\u20131321. ACM, Denver Colorado USA (2015)","DOI":"10.1145\/2810103.2813687"},{"key":"1045_CR49","doi-asserted-by":"publisher","first-page":"5827","DOI":"10.1109\/jiot.2019.2952146","volume":"7","author":"MAP Chamikara","year":"2020","unstructured":"Chamikara, M.A.P., Bertok, P., Khalil, I., Liu, D., Camtepe, S., Atiquzzaman, M.: Local differential privacy for deep learning. IEEE Internet Things J. 7, 5827\u20135842 (2020). https:\/\/doi.org\/10.1109\/jiot.2019.2952146","journal-title":"IEEE Internet Things J."},{"key":"1045_CR50","doi-asserted-by":"crossref","unstructured":"Zhang, X., Ding, J., Wu, M., Wong, S.T.C., Van Nguyen, H., Pan, M.: Adaptive privacy preserving deep learning algorithms for medical data. In: 2021 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 1168\u20131177. IEEE, Waikoloa, HI, USA (2021)","DOI":"10.1109\/WACV48630.2021.00121"},{"key":"1045_CR51","doi-asserted-by":"crossref","unstructured":"Li, A., Guo, J., Yang, H., Salim, F.D., Chen, Y.: DeepObfuscator: obfuscating intermediate representations with privacy-preserving adversarial learning on smartphones. In: Proceedings of the International Conference on Internet-of-Things Design and Implementation, pp. 28\u201339. ACM, (2021)","DOI":"10.1145\/3450268.3453519"},{"key":"1045_CR52","doi-asserted-by":"crossref","unstructured":"Roy, P.C., Boddeti, V.N.: Mitigating information leakage in image representations: a maximum entropy approach. In: 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2581\u20132589. IEEE, Long Beach, CA, USA (2019)","DOI":"10.1109\/CVPR.2019.00269"},{"key":"1045_CR53","unstructured":"Jeong, J., Cho, M., Benz, P., Hwang, J., Kim, J., Lee, S., Kim, T.-H.: Privacy safe representation learning via frequency filtering encoder. arXiv: arXiv:2208.02482 (2022)"},{"key":"1045_CR54","doi-asserted-by":"publisher","unstructured":"Liu, S., Du, J., Shrivastava, A., Zhong, L.: Privacy adversarial network: representation learning for mobile data privacy. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 3, 1\u201318 (2019). https:\/\/doi.org\/10.1145\/3369816","DOI":"10.1145\/3369816"},{"key":"1045_CR55","doi-asserted-by":"crossref","unstructured":"Wang, J., Zhang, J., Bao, W., Zhu, X., Cao, B., Yu, P.S.: Not just privacy: improving performance of private deep learning in mobile cloud. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2407\u20132416. ACM, London, UK (2018)","DOI":"10.1145\/3219819.3220106"},{"key":"1045_CR56","doi-asserted-by":"publisher","first-page":"374","DOI":"10.3390\/fi16100374","volume":"16","author":"HU Manzoor","year":"2024","unstructured":"Manzoor, H.U., Shabbir, A., Chen, A., Flynn, D., Zoha, A.: A survey of security strategies in federated learning: defending models, data, and privacy. Future Internet 16, 374 (2024). https:\/\/doi.org\/10.3390\/fi16100374","journal-title":"Future Internet"},{"key":"1045_CR57","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2023.106848","volume":"158","author":"N Khalid","year":"2023","unstructured":"Khalid, N., Qayyum, A., Bilal, M., Al-Fuqaha, A., Qadir, J.: Privacy-preserving artificial intelligence in healthcare: techniques and applications. Comput. Biol. Med. 158, 106848 (2023). https:\/\/doi.org\/10.1016\/j.compbiomed.2023.106848","journal-title":"Comput. Biol. Med."},{"key":"1045_CR58","unstructured":"Pereteanu, G.-L., Alansary, A., Passerat-Palmbach, J.: Split HE: fast secure inference combining split learning and homomorphic encryption. arXiv: arXiv:2202.13351 (2022)"},{"key":"1045_CR59","doi-asserted-by":"crossref","unstructured":"Khan, T., Budzys, M., Michalas, A.: Make split, not hijack: preventing feature-space hijacking attacks in split learning. In: Proceedings of the 29th ACM Symposium on Access Control Models and Technologies, pp. 19\u201330. ACM, San Antonio TX, USA (2024)","DOI":"10.1145\/3649158.3657039"},{"key":"1045_CR60","doi-asserted-by":"crossref","unstructured":"Liu, S., Xin, L., Lyu, X., Ren, C.: Masking-enabled data protection approach for accurate split learning. In: 2023 IEEE Wireless Communications and Networking Conference (WCNC), pp. 1\u20136. IEEE, Glasgow, UK (2023)","DOI":"10.1109\/WCNC55385.2023.10118971"},{"key":"1045_CR61","unstructured":"Chen, S., Khisti, A.: SECO: secure inference with model splitting across multi-server hierarchy. arXiv: arXiv:2404.16232 (2024)"},{"key":"1045_CR62","doi-asserted-by":"crossref","unstructured":"Malekzadeh, M., Kawsar, F.: Salted inference: enhancing privacy while maintaining efficiency of split inference in mobile computing. arXiv: arXiv:2310.13384 (2024)","DOI":"10.1145\/3638550.3641131"},{"key":"1045_CR63","doi-asserted-by":"publisher","first-page":"177","DOI":"10.1109\/mwc.015.2200462","volume":"31","author":"M Wu","year":"2024","unstructured":"Wu, M., Cheng, G., Li, P., Yu, R., Wu, Y., Pan, M., Lu, R.: Split learning with differential privacy for integrated terrestrial and non-terrestrial networks. IEEE Wirel. Commun. 31, 177\u2013184 (2024). https:\/\/doi.org\/10.1109\/mwc.015.2200462","journal-title":"IEEE Wirel. Commun."},{"key":"1045_CR64","doi-asserted-by":"publisher","first-page":"4713","DOI":"10.1007\/s11276-022-03113-7","volume":"30","author":"J Ryu","year":"2022","unstructured":"Ryu, J., Zheng, Y., Gao, Y., Abuadbba, A., Kim, J., Won, D., Nepal, S., Kim, H., Wang, C.: Can differential privacy practically protect collaborative deep learning inference for IoT? Wirel. Netw. 30, 4713\u20134733 (2022). https:\/\/doi.org\/10.1007\/s11276-022-03113-7","journal-title":"Wirel. Netw."},{"key":"1045_CR65","doi-asserted-by":"crossref","unstructured":"Wang, D., Ren, J., Xu, C., Liu, J., Wang, Z., Zhang, Y., Shen, X.: PrivStream: enabling privacy-preserving inferences on IoT data stream at the edge. In: 2019 IEEE 21st International Conference on High Performance Computing and Communications; IEEE 17th International Conference on Smart City; IEEE 5th International Conference on Data Science and Systems (HPCC\/SmartCity\/DSS), pp. 1290\u20131297. IEEE, Zhangjiajie, China (2019)","DOI":"10.1109\/HPCC\/SmartCity\/DSS.2019.00180"},{"key":"1045_CR66","doi-asserted-by":"crossref","unstructured":"Mao, Y., Yi, S., Li, Q., Feng, J., Xu, F., Zhong, S.: Learning from differentially private neural activations with edge computing. In: 2018 IEEE\/ACM Symposium on Edge Computing (SEC), pp. 90\u2013102. IEEE, Boston, MA (2018)","DOI":"10.1109\/SEC.2018.00014"},{"key":"1045_CR67","doi-asserted-by":"crossref","unstructured":"Roth, H.R., Hatamizadeh, A., Xu, Z., Zhao, C., Li, W., Myronenko, A., Xu, D.: Split-U-net: preventing data leakage in split learning for collaborative multi-modal brain tumor segmentation. arXiv: arXiv:2208.10553 (2022)","DOI":"10.1007\/978-3-031-18523-6_5"},{"key":"1045_CR68","unstructured":"Mai, P., Yan, R., Huang, Z., Yang, Y., Pang, Y.: Split-and-denoise: protect large language model inference with local differential privacy. arXiv: arXiv:2310.09130 (2023)"},{"key":"1045_CR69","unstructured":"Yang, X., Sun, J., Yao, Y., Xie, J., Wang, C.: Differentially private label protection in split learning. arXiv: arXiv:2203.02073 (2022)"},{"key":"1045_CR70","doi-asserted-by":"publisher","first-page":"1539","DOI":"10.1109\/tifs.2023.3243490","volume":"18","author":"Z Wang","year":"2023","unstructured":"Wang, Z., Yang, G., Dai, H., Rong, C.: Privacy-preserving split learning for large-scaled vision pre-training. IEEE Trans. Inf. Forensics Secur. 18, 1539\u20131553 (2023). https:\/\/doi.org\/10.1109\/tifs.2023.3243490","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"1045_CR71","unstructured":"Pham, N.D., Phan, K.T., Chilamkurti, N.: Enhancing accuracy-privacy trade-off in differentially private split learning. arXiv: arXiv:2310.14434 (2023)"},{"key":"1045_CR72","doi-asserted-by":"publisher","first-page":"6638","DOI":"10.1109\/jiot.2023.3311761","volume":"11","author":"M Odema","year":"2024","unstructured":"Odema, M., Al Faruque, M.A.: PrivyNAS: privacy-aware neural architecture search for split computing in edge\u2013cloud systems. IEEE Internet Things J. 11, 6638\u20136651 (2024). https:\/\/doi.org\/10.1109\/jiot.2023.3311761","journal-title":"IEEE Internet Things J."},{"key":"1045_CR73","doi-asserted-by":"crossref","unstructured":"Mireshghallah, F., Taram, M., Ramrakhyani, P., Jalali, A., Tullsen, D., Esmaeilzadeh, H.: Shredder: learning noise distributions to protect inference privacy. In: Proceedings of the Twenty-Fifth International Conference on Architectural Support for Programming Languages and Operating Systems, pp. 3\u201318. ACM, Lausanne, Switzerland (2020)","DOI":"10.1145\/3373376.3378522"},{"key":"1045_CR74","doi-asserted-by":"crossref","unstructured":"Mireshghallah, F., Taram, M., Jalali, A., Elthakeb, A.T.T., Tullsen, D., Esmaeilzadeh, H.: Not all features are equal: discovering essential features for preserving prediction privacy. In: Proceedings of the Web Conference 2021, pp. 669\u2013680. ACM, Ljubljana, Slovenia (2021)","DOI":"10.1145\/3442381.3449965"},{"key":"1045_CR75","doi-asserted-by":"crossref","unstructured":"Gao, R., Yang, H., Huang, S., Dun, M., Li, M., Luan, Z., Luan, Z., Qian, D.: PriPro: towards effective privacy protection on edge-cloud system running DNN inference. In: 2021 IEEE\/ACM 21st International Symposium on Cluster, Cloud and Internet Computing (CCGrid), pp. 334\u2013343. IEEE, Melbourne, Australia (2021)","DOI":"10.1109\/CCGrid51090.2021.00043"},{"key":"1045_CR76","doi-asserted-by":"crossref","unstructured":"Mao, Y., Xin, Z., Li, Z., Hong, J., Yang, Q., Zhong, S.: Secure split learning against property inference, data reconstruction, and\u00a0feature space hijacking attacks. arXiv: arXiv:2304.09515 (2023)","DOI":"10.1007\/978-3-031-51482-1_2"},{"key":"1045_CR77","doi-asserted-by":"crossref","unstructured":"Wan, X., Sun, J., Wang, S., Chen, L., Zheng, Z., Wu, F., Chen, G.: PSLF: defending against label leakage in split learning. In: Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, pp. 2492\u20132501. ACM, Birmingham, Uk (2023)","DOI":"10.1145\/3583780.3615019"},{"key":"1045_CR78","doi-asserted-by":"publisher","first-page":"3263","DOI":"10.1109\/tmc.2021.3050458","volume":"21","author":"W Du","year":"2022","unstructured":"Du, W., Li, A., Zhou, P., Niu, B., Wu, D.: Privacyeye: a privacy-preserving and computationally efficient deep learning-based mobile video analytics system. IEEE Trans. Mob. Comput. 21, 3263\u20133279 (2022). https:\/\/doi.org\/10.1109\/tmc.2021.3050458","journal-title":"IEEE Trans. Mob. Comput."},{"key":"1045_CR79","doi-asserted-by":"crossref","unstructured":"Alnasser, W., Beigi, G., Mosallanezhad, A., Liu, H.: PPSL: privacy-preserving text classification for split learning. In: 2022 4th International Conference on Data Intelligence and Security (ICDIS), pp. 160\u2013167. IEEE, Shenzhen, China (2022)","DOI":"10.1109\/ICDIS55630.2022.00032"},{"key":"1045_CR80","doi-asserted-by":"publisher","first-page":"10094","DOI":"10.1109\/ACCESS.2025.3528575","volume":"13","author":"A Alhindi","year":"2025","unstructured":"Alhindi, A., Al-Ahmadi, S., Ben Ismail, M.M.: Balancing privacy and utility in split learning: an adversarial channel pruning-based approach. IEEE Access 13, 10094\u201310110 (2025). https:\/\/doi.org\/10.1109\/ACCESS.2025.3528575","journal-title":"IEEE Access"},{"key":"1045_CR81","doi-asserted-by":"publisher","first-page":"4505","DOI":"10.1109\/jiot.2020.2967734","volume":"7","author":"SA Osia","year":"2020","unstructured":"Osia, S.A., Shamsabadi, A.S., Sajadmanesh, S., Taheri, A., Katevas, K., Rabiee, H.R., Lane, N.D., Haddadi, H.: A hybrid deep learning architecture for privacy-preserving mobile analytics. IEEE Internet Things J. 7, 4505\u20134518 (2020). https:\/\/doi.org\/10.1109\/jiot.2020.2967734","journal-title":"IEEE Internet Things J."},{"key":"1045_CR82","doi-asserted-by":"crossref","unstructured":"Vepakomma, P., Singh, A., Gupta, O., Raskar, R.: NoPeek: information leakage reduction to share activations in distributed deep learning. arXiv: arXiv:2008.09161 (2020)","DOI":"10.1109\/ICDMW51313.2020.00134"},{"key":"1045_CR83","doi-asserted-by":"crossref","unstructured":"Li, J., Liao, G., Chen, L., Chen, X.: Roulette: a semantic privacy-preserving device-edge collaborative inference framework for deep learning classification tasks. arXiv: arXiv:2309.02820 (2023)","DOI":"10.1109\/TMC.2023.3312304"},{"key":"1045_CR84","unstructured":"Titcombe, T., Hall, A.J., Papadopoulos, P., Romanini, D.: Practical defences against model inversion attacks for split neural networks. arXiv: arXiv:2104.05743 (2021)"},{"key":"1045_CR85","doi-asserted-by":"crossref","unstructured":"Dong, H., Yu, S., Wu, C., Guo, Y.: Semantic image synthesis via adversarial learning. In: 2017 IEEE International Conference on Computer Vision (ICCV), pp. 5707\u20135715. IEEE, Venice (2017)","DOI":"10.1109\/ICCV.2017.608"},{"key":"1045_CR86","doi-asserted-by":"publisher","first-page":"9706","DOI":"10.1109\/jiot.2020.3022358","volume":"8","author":"Z He","year":"2021","unstructured":"He, Z., Zhang, T., Lee, R.B.: Attacking and protecting data privacy in edge\u2013cloud collaborative inference systems. IEEE Internet Things J. 8, 9706\u20139716 (2021). https:\/\/doi.org\/10.1109\/jiot.2020.3022358","journal-title":"IEEE Internet Things J."},{"key":"1045_CR87","doi-asserted-by":"publisher","DOI":"10.1109\/tpds.2021.3139191","author":"D Xiao","year":"2022","unstructured":"Xiao, D., Yang, C., Wu, W.: Mixing activations and labels in distributed training for split learning. IEEE Trans. Parallel Distrib. Syst. (2022). https:\/\/doi.org\/10.1109\/tpds.2021.3139191","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"key":"1045_CR88","unstructured":"Baek, S., Park, J., Vepakomma, P., Raskar, R., Bennis, M., Kim, S.L.: Visual transformer meets cutmix for improved accuracy, communication efficiency, and data privacy in split learning. arXiv: arXiv:2207.00234 (2022)"},{"key":"1045_CR89","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., Houlsby, N.: An image is worth 16x16 words: transformers for image recognition at scale. arXiv: arXiv:2010.11929 (2021)"},{"key":"1045_CR90","unstructured":"Oh, S., Park, J., Baek, S., Nam, H., Vepakomma, P., Raskar, R., Bennis, M., Kim, S.-L.: Differentially private cutmix for split learning with vision transformer. arXiv: arXiv:2210.15986 (2022)"},{"key":"1045_CR91","doi-asserted-by":"crossref","unstructured":"Xu, H., Xiang, L., Ye, H., Yao, D., Chu, P., Li, B.: Permutation equivariance of transformers and its applications. arXiv: arXiv:2304.07735 (2024)","DOI":"10.1109\/CVPR52733.2024.00572"},{"key":"1045_CR92","unstructured":"MIT-BIH arrhythmia database. https:\/\/www.physionet.org\/content\/mitdb\/1.0.0\/. Accessed 6 November 2023"},{"key":"1045_CR93","unstructured":"PTB-XL, a large publicly available electrocardiography dataset. https:\/\/physionet.org\/content\/ptb-xl\/1.0.3\/. Accessed 6 November 2023"},{"key":"1045_CR94","unstructured":"The CIFAR dataset. https:\/\/www.cs.toronto.edu\/~kriz\/cifar.html. Accessed 24 October 2023"},{"key":"1045_CR95","unstructured":"MNIST dataset. http:\/\/yann.lecun.com\/exdb\/mnist\/. Accessed 23 October 2023"},{"key":"1045_CR96","unstructured":"PAMAP2 physical activity monitoring. https:\/\/archive.ics.uci.edu\/dataset\/231\/pamap2+physical+activity+monitoring. Accessed 26 November 2023"},{"key":"1045_CR97","unstructured":"The street view house numbers (SVHN) dataset. http:\/\/ufldl.stanford.edu\/housenumbers\/. Accessed 24 October 2023"},{"key":"1045_CR98","unstructured":"The German traffic sign recognition benchmark. https:\/\/benchmark.ini.rub.de\/gtsrb_news.html (2023). Accessed 24 October 2023"},{"key":"1045_CR99","unstructured":"STL-10 dataset. https:\/\/cs.stanford.edu\/~acoates\/stl10\/. Accessed 24 October 2023"},{"key":"1045_CR100","unstructured":"Medical segmentation decathlon. http:\/\/medicaldecathlon.com\/ (2023). Accessed 6 November 2023"},{"key":"1045_CR101","unstructured":"Azazu dataset. https:\/\/www.kaggle.com\/competitions\/avazu-ctr-prediction\/data. Accessed 6 November 2023"},{"key":"1045_CR102","unstructured":"Criteo dataset. https:\/\/ailab.criteo.com\/download-criteo-1tb-click-logs-dataset\/. Accessed 6 November 2023"},{"key":"1045_CR103","unstructured":"Large-scale celebfaces attributes (CelebA) dataset. https:\/\/mmlab.ie.cuhk.edu.hk\/projects\/CelebA.html. Accessed 24 October 2023"},{"key":"1045_CR104","unstructured":"Labeled faces in the wild home dataset. http:\/\/vis-www.cs.umass.edu\/lfw\/. Accessed 24 October 2023"},{"key":"1045_CR105","unstructured":"HAM10000 dataset. https:\/\/dataverse.harvard.edu\/dataset.xhtml?persistentId=doi:10.7910\/DVN\/DBW86T (2007). Accessed 7 November 2023"},{"key":"1045_CR106","unstructured":"ChestX-ray14. https:\/\/www.v7labs.com\/open-datasets\/chestx-ray14. Accessed 7 November 2023"},{"key":"1045_CR107","unstructured":"20 newsgroups dataset. https:\/\/www.kaggle.com\/datasets\/crawford\/20-newsgroups. Accessed 24 Octpber 2023"},{"key":"1045_CR108","unstructured":"UTKFace dataset. https:\/\/susanqq.github.io\/UTKFace\/. Accessed 24 Octpber 2023"},{"key":"1045_CR109","unstructured":"PASCAL VOC 2012 DATASET. https:\/\/www.kaggle.com\/datasets\/gopalbhattrai\/pascal-voc-2012-dataset. Accessed 29 November 2023"},{"key":"1045_CR110","unstructured":"MovieLens. https:\/\/grouplens.org\/datasets\/movielens\/. Accessed 7 November 2023"},{"key":"1045_CR111","unstructured":"Book-crossing. https:\/\/grouplens.org\/datasets\/book-crossing\/. Accessed 7 November 2023"},{"key":"1045_CR112","unstructured":"imageNet dataset. https:\/\/www.image-net.org\/. Accessed 24 October 2023"},{"key":"1045_CR113","unstructured":"Human Action Recognition (HAR) dataset. https:\/\/www.kaggle.com\/datasets\/meetnagadia\/human-action-recognition-har-dataset. Accessed 24 October 2023"},{"key":"1045_CR114","unstructured":"State farm distracted driver detection dataset. https:\/\/www.kaggle.com\/c\/state-farm-distracted-driver-detection\/data. Accessed 24 October 2023"},{"key":"1045_CR115","unstructured":"Extended yale B dataset. https:\/\/academictorrents.com\/details\/06e479f338b56fa5948c40287b66f68236a14612. Accessed 24 October 2023"},{"key":"1045_CR116","unstructured":"TrustPilot. https:\/\/www.kaggle.com\/datasets\/crawlfeeds\/trustpilot-reviews-dataset. Accessed 7 November 2023"},{"key":"1045_CR117","unstructured":"PEViD-HD: privacy evaluation video dataset. https:\/\/www.epfl.ch\/labs\/mmspg\/downloads\/pevid-hd\/. Accessed 9 December 2023"},{"key":"1045_CR118","unstructured":"Fairface dataset. https:\/\/github.com\/dchen236\/FairFace. Accessed 24 October 2023"},{"key":"1045_CR119","unstructured":"IMDB-WIKI dataset. https:\/\/data.vision.ee.ethz.ch\/cvl\/rrothe\/imdb-wiki\/. Accessed 13 November 2023"},{"key":"1045_CR120","unstructured":"Diabetic retinopathy dataset. https:\/\/www.kaggle.com\/c\/diabetic-retinopathy-detection\/data. Accessed 24 October 2023"},{"key":"1045_CR121","unstructured":"Fashion MNIST. https:\/\/www.kaggle.com\/datasets\/zalando-research\/fashionmnist. Accessed 24 October 2023"},{"key":"1045_CR122","unstructured":"EMNIST (extended MNIST). https:\/\/www.kaggle.com\/datasets\/crawford\/emnist. Accessed 17 December 2023"},{"key":"1045_CR123","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3168389","volume":"51","author":"I Wagner","year":"2018","unstructured":"Wagner, I., Eckhoff, D.: Technical privacy metrics: a systematic survey. ACM Comput. Surv. 51, 1\u201338 (2018). https:\/\/doi.org\/10.1145\/3168389","journal-title":"ACM Comput. Surv."},{"key":"1045_CR124","unstructured":"Maeng, K., Guo, C., Kariyappa, S., Suh, E.: Measuring and controlling split layer privacy leakage using fisher information. arXiv: arXiv:2209.10119 (2022)"}],"container-title":["International Journal of Information Security"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10207-025-01045-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10207-025-01045-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10207-025-01045-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,21]],"date-time":"2025-06-21T10:11:01Z","timestamp":1750500661000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10207-025-01045-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,7]]},"references-count":124,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2025,6]]}},"alternative-id":["1045"],"URL":"https:\/\/doi.org\/10.1007\/s10207-025-01045-9","relation":{},"ISSN":["1615-5262","1615-5270"],"issn-type":[{"value":"1615-5262","type":"print"},{"value":"1615-5270","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5,7]]},"assertion":[{"value":"18 April 2025","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 May 2025","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"125"}}