{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T00:45:13Z","timestamp":1767314713140,"version":"3.48.0"},"publisher-location":"Singapore","reference-count":22,"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_25","type":"book-chapter","created":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T00:41:44Z","timestamp":1767314504000},"page":"362-376","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Verifiable and\u00a0Privacy-Preserving Credit Risk Prediction Under Secure Neural Network"],"prefix":"10.1007","author":[{"given":"Jianyu","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mengya","family":"Lei","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhen","family":"Fan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mingwu","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,1,2]]},"reference":[{"issue":"12","key":"25_CR1","doi-asserted-by":"publisher","first-page":"18217","DOI":"10.1007\/s11042-022-13952-3","volume":"82","author":"A Bhattacharya","year":"2023","unstructured":"Bhattacharya, A., Biswas, S.K., Mandal, A.: Credit risk evaluation: a comprehensive study. Multimedia Tools Appl. 82(12), 18217\u201318267 (2023)","journal-title":"Multimedia Tools Appl."},{"issue":"1","key":"25_CR2","first-page":"7","volume":"6","author":"A Motwani","year":"2018","unstructured":"Motwani, A., Chaurasiya, P.-K., Bajaj, G.: Predicting credit worthiness of bank customer with machine learning over cloud. Int. J. Comput. Sci. Eng. 6(1), 7 (2018)","journal-title":"Int. J. Comput. Sci. Eng."},{"issue":"1","key":"25_CR3","doi-asserted-by":"publisher","first-page":"1592","DOI":"10.1109\/JSYST.2020.3045076","volume":"16","author":"C Lin","year":"2021","unstructured":"Lin, C., Luo, M., Huang, X.-Y.: An efficient privacy-preserving credit score system based on noninteractive zero-knowledge proof. IEEE Syst. J. 16(1), 1592\u20131601 (2021)","journal-title":"IEEE Syst. J."},{"key":"25_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.115989","volume":"188","author":"Y-C Qiao","year":"2022","unstructured":"Qiao, Y.-C., Lan, Q.-J., Zhou, Z.-D.: Privacy-preserving credit evaluation system based on blockchain. Expert Syst. Appl. 188, 115989 (2022)","journal-title":"Expert Syst. Appl."},{"key":"25_CR5","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1007\/978-3-030-87010-2_3","volume-title":"Computational Science and Its Applications \u2013 ICCSA 2021","author":"L Andolfo","year":"2021","unstructured":"Andolfo, L., et al.: Privacy-preserving credit scoring via\u00a0functional encryption. In: Gervasi, O., et al. (eds.) ICCSA 2021. LNCS, vol. 12956, pp. 31\u201343. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87010-2_3"},{"key":"25_CR6","doi-asserted-by":"publisher","first-page":"5804","DOI":"10.1109\/TIFS.2023.3315526","volume":"18","author":"M Zhang","year":"2023","unstructured":"Zhang, M., Chen, S., Shen, J., Susilo, W.: PrivacyEAFL: privacy-enhanced aggregation for federated learning in mobile crowdsensing. IEEE Trans. Inf. Forensics Secur. 18, 5804\u20135816 (2023)","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"25_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.dss.2022.113910","volume":"166","author":"H-R He","year":"2023","unstructured":"He, H.-R., Wang, Z., Jain, H.: A privacy-preserving decentralized credit scoring method based on multi-party information. Decis. Support Syst. 166, 113910 (2023)","journal-title":"Decis. Support Syst."},{"key":"25_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.frl.2024.105937","volume":"67","author":"X-H Kuang","year":"2024","unstructured":"Kuang, X.-H., Ma, C.-Q., Ren, Y.-S.: Credit risk: a new privacy-preserving decentralized credit assessment model. Financ. Res. Lett. 67, 105937 (2024)","journal-title":"Financ. Res. Lett."},{"key":"25_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.jnca.2020.102754","volume":"167","author":"X Ma","year":"2020","unstructured":"Ma, X., Ji, C.-M., Zhang, X.-Y.: Secure multiparty learning from the aggregation of locally trained models. J. Netw. Comput. Appl. 167, 102754 (2020)","journal-title":"J. Netw. Comput. Appl."},{"issue":"1","key":"25_CR10","first-page":"1051","volume":"6","author":"VVLD Allavarpu","year":"2025","unstructured":"Allavarpu, V.V.L.D., Naresh, V.-S., Mohan, A.-K.: Neural network-driven privacy-preserving credit risk analysis: a homomorphic encryption approach. Contemp. Math. 6(1), 1051\u20131075 (2025)","journal-title":"Contemp. Math."},{"issue":"1","key":"25_CR11","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1186\/s13677-024-00711-y","volume":"13","author":"V-S Naresh","year":"2024","unstructured":"Naresh, V.-S.: PPDNN-CRP: privacy-preserving deep neural network processing for credit risk prediction in cloud: a homomorphic encryption-based approach. J. Cloud Comput. 13(1), 149 (2024)","journal-title":"J. Cloud Comput."},{"key":"25_CR12","doi-asserted-by":"publisher","first-page":"507","DOI":"10.1016\/j.ins.2018.12.015","volume":"481","author":"X Ma","year":"2019","unstructured":"Ma, X., Chen, X.-F., Zhang, X.-Y.: Non-interactive privacy-preserving neural network prediction. Inf. Sci. 481, 507\u2013519 (2019)","journal-title":"Inf. Sci."},{"issue":"2","key":"25_CR13","doi-asserted-by":"publisher","first-page":"659","DOI":"10.1109\/TSC.2023.3332744","volume":"17","author":"W Huang","year":"2023","unstructured":"Huang, W., Zhang, G.-L., Liao, Y.-J.: Secure neural network prediction in the cloud-based open neural network service. IEEE Trans. Serv. Comput. 17(2), 659\u2013673 (2023)","journal-title":"IEEE Trans. Serv. Comput."},{"key":"25_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.csi.2022.103678","volume":"84","author":"M-W Zhang","year":"2023","unstructured":"Zhang, M.-W., Huang, S., Shen, G.: PPNNP: a privacy-preserving neural network prediction with separated data providers using multi-client inner-product encryption. Comput. Standards Interfaces 84, 103678 (2023)","journal-title":"Comput. Standards Interfaces"},{"key":"25_CR15","doi-asserted-by":"crossref","unstructured":"Yao, Y.-Y., Zhao, Z.-D., Chang, X.-L.: A novel privacy-preserving neural network computing approach for e-health information system. In: IEEE International Conference on Communications (ICC), pp. 1\u20136. IEEE (2021)","DOI":"10.1109\/ICC42927.2021.9500795"},{"key":"25_CR16","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"733","DOI":"10.1007\/978-3-662-46447-2_33","volume-title":"Public-Key Cryptography \u2013 PKC 2015","author":"M Abdalla","year":"2015","unstructured":"Abdalla, M., Bourse, F., De Caro, A., Pointcheval, D.: Simple functional encryption schemes for inner products. In: Katz, J. (ed.) PKC 2015. LNCS, vol. 9020, pp. 733\u2013751. Springer, Heidelberg (2015). https:\/\/doi.org\/10.1007\/978-3-662-46447-2_33"},{"key":"25_CR17","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"354","DOI":"10.1007\/978-3-319-26059-4_20","volume-title":"Provable Security","author":"B David","year":"2015","unstructured":"David, B., Dowsley, R., Katti, R., Nascimento, A.C.A.: Efficient unconditionally secure comparison and privacy preserving machine learning classification protocols. In: Au, M.-H., Miyaji, A. (eds.) ProvSec 2015. LNCS, vol. 9451, pp. 354\u2013367. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-26059-4_20"},{"key":"25_CR18","doi-asserted-by":"publisher","unstructured":"Australian Credit Approval. https:\/\/archive.ics.uci.edu\/dataset\/143\/statlog+australian+credit+approval. https:\/\/doi.org\/10.24432\/C59012(2025)","DOI":"10.24432\/C59012"},{"issue":"6","key":"25_CR19","doi-asserted-by":"publisher","first-page":"518","DOI":"10.1109\/TC.1984.1676475","volume":"100","author":"K-H Huang","year":"1984","unstructured":"Huang, K.-H., Abraham, J.A.: Algorithm-based fault tolerance for matrix operations. IEEE Trans. Comput. 100(6), 518\u2013528 (1984)","journal-title":"IEEE Trans. Comput."},{"key":"25_CR20","doi-asserted-by":"publisher","first-page":"289","DOI":"10.1016\/j.knosys.2014.10.010","volume":"73","author":"D Liang","year":"2015","unstructured":"Liang, D., Tsai, C.-F., Wu, H.-T.: The effect of feature selection on financial distress prediction. Knowl.-Based Syst. 73, 289\u2013297 (2015)","journal-title":"Knowl.-Based Syst."},{"key":"25_CR21","doi-asserted-by":"crossref","unstructured":"Panzade, P., Takabi, D.: Towards faster functional encryption for privacy-preserving machine learning. In: 2021 Third IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA), pp. 21\u201330. IEEE (2021)","DOI":"10.1109\/TPSISA52974.2021.00003"},{"key":"25_CR22","unstructured":"Gilad-Bachrach, R., Dowlin, N., Laine, K.: CryptoNets: applying neural networks to encrypted data with high throughput and accuracy. In: Proceedings of the 33nd International Conference on Machine Learning (PMLR), pp. 201\u2013210 (2016)"}],"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_25","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T00:41:46Z","timestamp":1767314506000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-3551-4_25"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9789819535507","9789819535514"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-3551-4_25","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"}}]}}