{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T00:39:24Z","timestamp":1767314364192,"version":"3.48.0"},"publisher-location":"Singapore","reference-count":32,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819535507","type":"print"},{"value":"9789819535514","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"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":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-981-95-3551-4_10","type":"book-chapter","created":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T00:34:51Z","timestamp":1767314091000},"page":"137-151","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Privacy-Preserving Machine Learning Using Functional Encryptions for\u00a0Multiple Models with\u00a0Constant Ciphertext"],"prefix":"10.1007","author":[{"given":"Yizhen","family":"Hua","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9754-0008","authenticated-orcid":false,"given":"Jiangtao","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7279-1783","authenticated-orcid":false,"given":"Yufeng","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,1,2]]},"reference":[{"issue":"4","key":"10_CR1","doi-asserted-by":"publisher","first-page":"291","DOI":"10.2174\/1389202922666210705124359","volume":"22","author":"H Habehh","year":"2021","unstructured":"Habehh, H., Gohel, S.: Machine learning in healthcare. Curr. Genomics 22(4), 291\u2013300 (2021)","journal-title":"Curr. Genomics"},{"key":"10_CR2","doi-asserted-by":"crossref","unstructured":"Shailaja, K., Seetharamulu, B., Jabbar, M.: Machine learning in healthcare: a review. In: 2018 Second International Conference on Electronics, Communication and Aerospace Technology (ICECA), pp.\u00a0910\u2013914. IEEE (2018)","DOI":"10.1109\/ICECA.2018.8474918"},{"key":"10_CR3","doi-asserted-by":"crossref","unstructured":"Jaihar, J., Lingayat, N., Vijaybhai, P.S., Venkatesh, G., Upla, K.P.: Smart home automation using machine learning algorithms. In: 2020 International Conference for Emerging Technology (INCET), pp.\u00a01\u20134. IEEE (2020)","DOI":"10.1109\/INCET49848.2020.9154007"},{"key":"10_CR4","doi-asserted-by":"crossref","unstructured":"Alzoubi, A.: Machine learning for intelligent energy consumption in smart homes. Int. J. Comput. Inf. Manuf. (IJCIM) 2(1) (2022)","DOI":"10.54489\/ijcim.v2i1.75"},{"key":"10_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.cosrev.2021.100395","volume":"40","author":"TK Balaji","year":"2021","unstructured":"Balaji, T.K., Annavarapu, C.S.R., Bablani, A.: Machine learning algorithms for social media analysis: a survey. Comput. Sci. Rev. 40, 100395 (2021)","journal-title":"Comput. Sci. Rev."},{"key":"10_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2020.103500","volume":"108","author":"A Gupta","year":"2020","unstructured":"Gupta, A., Katarya, R.: Social media based surveillance systems for healthcare using machine learning: a systematic review. J. Biomed. Inform. 108, 103500 (2020)","journal-title":"J. Biomed. Inform."},{"issue":"1","key":"10_CR7","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1007\/s13278-022-01020-5","volume":"13","author":"M Aljabri","year":"2023","unstructured":"Aljabri, M., Zagrouba, R., Shaahid, A., Alnasser, F., Saleh, A., Alomari, D.M.: Machine learning-based social media bot detection: a comprehensive literature review. Soc. Netw. Anal. Min. 13(1), 20 (2023)","journal-title":"Soc. Netw. Anal. Min."},{"key":"10_CR8","volume":"6","author":"MR Bachute","year":"2021","unstructured":"Bachute, M.R., Subhedar, J.M.: Autonomous driving architectures: insights of machine learning and deep learning algorithms. Mach. Learn. Appl. 6, 100164 (2021)","journal-title":"Mach. Learn. Appl."},{"key":"10_CR9","doi-asserted-by":"crossref","unstructured":"Yu, H., Huo, S., Zhu, M., Gong, Y., Xiang, Y.: Machine learning-based vehicle intention trajectory recognition and prediction for autonomous driving. In: 2024 7th International Conference on Advanced Algorithms and Control Engineering (ICAACE), pp.\u00a0771\u2013775. IEEE (2024)","DOI":"10.1109\/ICAACE61206.2024.10548252"},{"key":"10_CR10","doi-asserted-by":"crossref","unstructured":"Lee, J.-W., et\u00a0al.: Privacy-preserving machine learning with fully homomorphic encryption for deep neural network. IEEE Access 10, 30039\u201330054 (2022)","DOI":"10.1109\/ACCESS.2022.3159694"},{"issue":"3","key":"10_CR11","doi-asserted-by":"publisher","first-page":"1666","DOI":"10.1007\/s12083-021-01076-8","volume":"14","author":"B Pulido-Gaytan","year":"2021","unstructured":"Pulido-Gaytan, B., et al.: Privacy-preserving neural networks with homomorphic encryption: challenges and opportunities. Peer-to-Peer Network Appl. 14(3), 1666\u20131691 (2021)","journal-title":"Peer-to-Peer Network Appl."},{"key":"10_CR12","doi-asserted-by":"crossref","unstructured":"Marcano, N.J.H., Moller, M., Hansen, S., Jacobsen, R.H.: On fully homomorphic encryption for privacy-preserving deep learning. In: 2019 IEEE Globecom Workshops (GC Wkshps), pp.\u00a01\u20136. IEEE (2019)","DOI":"10.1109\/GCWkshps45667.2019.9024625"},{"key":"10_CR13","doi-asserted-by":"publisher","first-page":"523","DOI":"10.1016\/j.procs.2022.11.100","volume":"213","author":"S Zapechnikov","year":"2022","unstructured":"Zapechnikov, S.: Secure multi-party computations for privacy-preserving machine learning. Procedia Comput. Sci. 213, 523\u2013527 (2022)","journal-title":"Procedia Comput. Sci."},{"key":"10_CR14","doi-asserted-by":"crossref","unstructured":"Sayyad, S.: Privacy preserving deep learning using secure multiparty computation. In: 2020 Second International Conference on Inventive Research in Computing Applications (ICIRCA), pp.\u00a0139\u2013142. IEEE (2020)","DOI":"10.1109\/ICIRCA48905.2020.9183133"},{"key":"10_CR15","unstructured":"Duan, S., et al.: SSNet: a lightweight multi-party computation scheme for practical privacy-preserving machine learning service in the cloud. arXiv preprint arXiv:2406.02629 (2024)"},{"key":"10_CR16","unstructured":"Narra, K.G., Lin, Z., Wang, Y., Balasubramaniam, K., Annavaram, M.: Privacy-preserving inference in machine learning services using trusted execution environments. arXiv preprint arXiv:1912.03485 (2019)"},{"key":"10_CR17","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1016\/j.ins.2020.02.037","volume":"522","author":"Y Chen","year":"2020","unstructured":"Chen, Y., Luo, F., Li, T., Xiang, T., Liu, Z., Li, J.: A training-integrity privacy-preserving federated learning scheme with trusted execution environment. Inf. Sci. 522, 69\u201379 (2020)","journal-title":"Inf. Sci."},{"key":"10_CR18","doi-asserted-by":"crossref","unstructured":"Mo, F., Haddadi, H., Katevas, K., Marin, E., Perino, D., Kourtellis, N.: PPFL: privacy-preserving federated learning with trusted execution environments. In: Proceedings of the 19th Annual International Conference on Mobile Systems, Applications, and Services, pp.\u00a094\u2013108 (2021)","DOI":"10.1145\/3458864.3466628"},{"key":"10_CR19","unstructured":"Ryffel, T., Dufour-Sans, E., Gay, R., Bach, F., Pointcheval, D.: Partially encrypted machine learning using functional encryption. In: NeurIPS 2019-Thirty-third Conference on Neural Information Processing Systems (2019)"},{"key":"10_CR20","doi-asserted-by":"crossref","unstructured":"Abdalla, M., Gong, J., Wee, H.: Functional encryption for attribute-weighted sums from $$k$$-Lin. In: Annual International Cryptology Conference, pp.\u00a0685\u2013716, Springer (2020)","DOI":"10.1007\/978-3-030-56784-2_23"},{"key":"10_CR21","doi-asserted-by":"crossref","unstructured":"Baltico, C.E.Z., Catalano, D., Fiore, D., Gay, R.: Practical functional encryption for quadratic functions with applications to predicate encryption. In: Annual International Cryptology Conference, pp.\u00a067\u201398, Springer (2017)","DOI":"10.1007\/978-3-319-63688-7_3"},{"key":"10_CR22","doi-asserted-by":"crossref","unstructured":"Zhu, Z., Li, J., Zhang, K., Gong, J., Qian, H.: Registered functional encryptions from pairings. In: Annual International Conference on the Theory and Applications of Cryptographic Techniques, pp.\u00a0373\u2013402. Springer (2024)","DOI":"10.1007\/978-3-031-58723-8_13"},{"key":"10_CR23","doi-asserted-by":"crossref","unstructured":"Datta, P., Pal, T., Yamada, S.: Registered FE beyond predicates:(attribute-based) linear functions and more. In: International Conference on the Theory and Application of Cryptology and Information Security, pp.\u00a065\u2013104. Springer (2025)","DOI":"10.1007\/978-981-96-0875-1_3"},{"key":"10_CR24","doi-asserted-by":"crossref","unstructured":"Gentry, C.: Fully homomorphic encryption using ideal lattices. In: Proceedings of the Forty-First Annual ACM Symposium on Theory of Computing, pp.\u00a0169\u2013178 (2009)","DOI":"10.1145\/1536414.1536440"},{"key":"10_CR25","unstructured":"Hesamifard, E., Takabi, H., Ghasemi, M.: CryptoDL: deep neural networks over encrypted data. arXiv preprint arXiv:1711.05189 (2017)"},{"key":"10_CR26","unstructured":"Takabi, H., Hesamifard, E., Ghasemi, M.: Privacy preserving multi-party machine learning with homomorphic encryption. In: 29th Annual Conference on Neural Information Processing Systems (NIPS), vol.\u00a01, p.\u00a04 (2016)"},{"key":"10_CR27","unstructured":"Juvekar, C., Vaikuntanathan, V., Chandrakasan, A.: $$\\{$$GAZELLE$$\\}$$: a low latency framework for secure neural network inference. In: 27th USENIX Security Symposium (USENIX security 18), pp.\u00a01651\u20131669 (2018)"},{"key":"10_CR28","doi-asserted-by":"crossref","unstructured":"Mohassel, P., Zhang, Y.: SecureML: a system for scalable privacy-preserving machine learning. In: 2017 IEEE Symposium on Security and Privacy (SP), pp.\u00a019\u201338. IEEE (2017)","DOI":"10.1109\/SP.2017.12"},{"key":"10_CR29","doi-asserted-by":"crossref","unstructured":"Ligier, D., Carpov, S., Fontaine, C., Sirdey, R.: Privacy preserving data classification using inner-product functional encryption. In: Proceedings of the 3rd International Conference on Information Systems Security and Privacy - Volume 1: ICISSP, pp.\u00a0423\u2013430, INSTICC, SciTePress (2017)","DOI":"10.5220\/0006206704230430"},{"key":"10_CR30","doi-asserted-by":"crossref","unstructured":"Xu, R., Joshi, J.B., Li, C.: CryptoNN: training neural networks over encrypted data. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp.\u00a01199\u20131209. IEEE (2019)","DOI":"10.1109\/ICDCS.2019.00121"},{"key":"10_CR31","unstructured":"Dufour-Sans, E., Gay, R., Pointcheval, D.: Reading in the dark: classifying encrypted digits with functional encryption. Cryptol. ePrint Arch. (2018)"},{"key":"10_CR32","doi-asserted-by":"crossref","unstructured":"Panzade, P., Takabi, D.: FENet: privacy-preserving neural network training with functional encryption. In: Proceedings of the 9th ACM International Workshop on Security and Privacy Analytics, pp.\u00a033\u201343 (2023)","DOI":"10.1145\/3579987.3586566"}],"container-title":["Lecture Notes in Computer Science","Cyberspace Safety and Security"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-3551-4_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T00:34:54Z","timestamp":1767314094000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-3551-4_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9789819535507","9789819535514"],"references-count":32,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-3551-4_10","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"2 January 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CSS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Symposium on Cyberspace Safety and Security","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Hangzhou","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 July 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 July 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"css2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/nsclab.org\/css2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}