{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,3]],"date-time":"2026-02-03T21:57:09Z","timestamp":1770155829916,"version":"3.49.0"},"publisher-location":"Singapore","reference-count":28,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819564187","type":"print"},{"value":"9789819564194","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\/978-981-95-6419-4_4","type":"book-chapter","created":{"date-parts":[[2026,2,3]],"date-time":"2026-02-03T08:57:49Z","timestamp":1770109069000},"page":"59-76","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Secure and\u00a0Privacy-Preserving Load Forecasting in\u00a0Smart Grids Through Blockchain-Based Incentives"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-0014-0065","authenticated-orcid":false,"given":"Joan","family":"Ferr\u00e9-Queralt","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0037-9888","authenticated-orcid":false,"given":"Jordi","family":"Castell\u00e0-Roca","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2342-5100","authenticated-orcid":false,"given":"Alexandre","family":"Viejo","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,2,4]]},"reference":[{"key":"4_CR1","unstructured":"Agency, I.E.: Global electricity demand rising in 2021. IEA (2021), https:\/\/www.iea.org\/reports\/global-energy-review-2021"},{"key":"4_CR2","doi-asserted-by":"publisher","first-page":"71054","DOI":"10.1109\/ACCESS.2022.3187839","volume":"10","author":"N Ahmad","year":"2022","unstructured":"Ahmad, N., Ghadi, Y., Adnan, M., Ali, M.: Load forecasting techniques for power system: Research challenges and survey. IEEE Access 10, 71054\u201371090 (2022)","journal-title":"IEEE Access"},{"key":"4_CR3","doi-asserted-by":"crossref","unstructured":"Amral, N., Ozveren, C., King, D.: Short term load forecasting using multiple linear regression. In: 2007 42nd International universities power engineering conference. pp. 1192\u20131198. IEEE (2007)","DOI":"10.1109\/UPEC.2007.4469121"},{"key":"4_CR4","doi-asserted-by":"crossref","unstructured":"Bilgin, Z., Tomur, E., Ersoy, M.A., Soykan, E.U.: Statistical appliance inference in the smart grid by machine learning. In: 2019 IEEE 30th International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC Workshops). pp.\u00a01\u20137. IEEE (2019)","DOI":"10.1109\/PIMRCW.2019.8880846"},{"key":"4_CR5","unstructured":"Blanchard, P., El\u00a0Mhamdi, E.M., Guerraoui, R., Stainer, J.: Machine learning with adversaries: Byzantine tolerant gradient descent. Advances in neural information processing systems 30 (2017)"},{"key":"4_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2021.104468","volume":"106","author":"A Blanco-Justicia","year":"2021","unstructured":"Blanco-Justicia, A., Domingo-Ferrer, J., Mart\u00ednez, S., S\u00e1nchez, D., Flanagan, A., Tan, K.E.: Achieving security and privacy in federated learning systems: Survey, research challenges and future directions. Eng. Appl. Artif. Intell. 106, 104468 (2021)","journal-title":"Eng. Appl. Artif. Intell."},{"key":"4_CR7","doi-asserted-by":"publisher","first-page":"602","DOI":"10.1016\/j.apenergy.2014.07.064","volume":"132","author":"J Che","year":"2014","unstructured":"Che, J., Wang, J.: Short-term load forecasting using a kernel-based support vector regression combination model. Appl. Energy 132, 602\u2013609 (2014)","journal-title":"Appl. Energy"},{"key":"4_CR8","doi-asserted-by":"crossref","unstructured":"Dudek, G.: Short-term load forecasting using random forests. In: Intelligent Systems\u2019 2014: Proceedings of the 7th IEEE International Conference Intelligent Systems IS\u20192014, September 24-26, 2014, Warsaw, Poland, Volume 2: Tools, Architectures, Systems, Applications. pp. 821\u2013828. Springer (2015)","DOI":"10.1007\/978-3-319-11310-4_71"},{"key":"4_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijepes.2021.107669","volume":"137","author":"MN Fekri","year":"2022","unstructured":"Fekri, M.N., Grolinger, K., Mir, S.: Distributed load forecasting using smart meter data: Federated learning with recurrent neural networks. International Journal of Electrical Power & Energy Systems 137, 107669 (2022)","journal-title":"International Journal of Electrical Power & Energy Systems"},{"key":"4_CR10","unstructured":"Gayoso\u00a0Mart\u00ednez, V., Hern\u00e1ndez\u00a0Encinas, L., S\u00e1nchez\u00a0\u00c1vila, C.: A survey of the elliptic curve integrated encryption scheme. JOURNAL OF COMPUTER SCIENCE AND ENGINEERING (2010)"},{"key":"4_CR11","doi-asserted-by":"crossref","unstructured":"He, Y., Luo, F., Ranzi, G., Kong, W.: Short-term residential load forecasting based on federated learning and load clustering. In: 2021 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm). pp. 77\u201382. IEEE (2021)","DOI":"10.1109\/SmartGridComm51999.2021.9632314"},{"issue":"6","key":"4_CR12","doi-asserted-by":"publisher","first-page":"4409","DOI":"10.1109\/TSG.2023.3268633","volume":"14","author":"Y He","year":"2023","unstructured":"He, Y., Luo, F., Sun, M., Ranzi, G.: Privacy-preserving and hierarchically federated framework for short-term residential load forecasting. IEEE Transactions on Smart Grid 14(6), 4409\u20134423 (2023)","journal-title":"IEEE Transactions on Smart Grid"},{"key":"4_CR13","unstructured":"Jebreel, N.M., Domingo-Ferrer, J., Blanco-Justicia, A., S\u00e1nchez, D.: Enhanced security and privacy via fragmented federated learning. IEEE Transactions on Neural Networks and Learning Systems (2022)"},{"issue":"1","key":"4_CR14","doi-asserted-by":"publisher","first-page":"841","DOI":"10.1109\/TSG.2017.2753802","volume":"10","author":"W Kong","year":"2017","unstructured":"Kong, W., Dong, Z.Y., Jia, Y., Hill, D.J., Xu, Y., Zhang, Y.: Short-term residential load forecasting based on lstm recurrent neural network. IEEE transactions on smart grid 10(1), 841\u2013851 (2017)","journal-title":"IEEE transactions on smart grid"},{"key":"4_CR15","doi-asserted-by":"publisher","first-page":"1040","DOI":"10.1016\/j.enconman.2015.07.041","volume":"103","author":"A Lahouar","year":"2015","unstructured":"Lahouar, A., Slama, J.B.H.: Day-ahead load forecast using random forest and expert input selection. Energy Convers. Manage. 103, 1040\u20131051 (2015)","journal-title":"Energy Convers. Manage."},{"key":"4_CR16","doi-asserted-by":"crossref","unstructured":"Lei, J., Wang, L., Pei, Q., Sun, W., Lin, X., Liu, X.: Privgrid: Privacy-preserving individual load forecasting service for smart grid. IEEE Transactions on Information Forensics and Security (2024)","DOI":"10.1109\/TIFS.2024.3422876"},{"key":"4_CR17","unstructured":"McMahan, H.B., Moore, E., Ramage, D., Hampson, S., Ag\u00fcera\u00a0y Arcas, B.: Communication-efficient learning of deep networks from decentralized data. In: 20th International Conference on Artificial In telligence and Statistics (AISTATS) (2017)"},{"key":"4_CR18","doi-asserted-by":"crossref","unstructured":"Melis, L., Song, C., De\u00a0Cristofaro, E., Shmatikov, V.: Exploiting unintended feature leakage in collaborative learning. In: 2019 IEEE symposium on security and privacy (SP). pp. 691\u2013706. IEEE (2019)","DOI":"10.1109\/SP.2019.00029"},{"issue":"1","key":"4_CR19","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1109\/JIOT.2020.2993601","volume":"8","author":"MB Mollah","year":"2020","unstructured":"Mollah, M.B., Zhao, J., Niyato, D., Lam, K.Y., Zhang, X., Ghias, A.M., Koh, L.H., Yang, L.: Blockchain for future smart grid: A comprehensive survey. IEEE Internet Things J. 8(1), 18\u201343 (2020)","journal-title":"IEEE Internet Things J."},{"key":"4_CR20","doi-asserted-by":"publisher","first-page":"95949","DOI":"10.1109\/ACCESS.2021.3094089","volume":"9","author":"M Savi","year":"2021","unstructured":"Savi, M., Olivadese, F.: Short-term energy consumption forecasting at the edge: A federated learning approach. IEEE Access 9, 95949\u201395969 (2021)","journal-title":"IEEE Access"},{"key":"4_CR21","doi-asserted-by":"crossref","unstructured":"Selvam, C., Srinivas, K., Ayyappan, G., Sarma, M.V.: Advanced metering infrastructure for smart grid applications. In: 2012 International Conference on Recent Trends in Information Technology. pp. 145\u2013150. IEEE (2012)","DOI":"10.1109\/ICRTIT.2012.6206777"},{"key":"4_CR22","doi-asserted-by":"crossref","unstructured":"Soykan, E.U., Bilgin, Z., Ersoy, M.A., Tomur, E.: Differentially private deep learning for load forecasting on smart grid. In: 2019 IEEE Globecom Workshops (GC Wkshps). pp.\u00a01\u20136. IEEE (2019)","DOI":"10.1109\/GCWkshps45667.2019.9024520"},{"key":"4_CR23","doi-asserted-by":"crossref","unstructured":"Ta\u00efk, A., Cherkaoui, S.: Electrical load forecasting using edge computing and federated learning. In: ICC 2020-2020 IEEE international conference on communications (ICC). pp.\u00a01\u20136. IEEE (2020)","DOI":"10.1109\/ICC40277.2020.9148937"},{"issue":"6","key":"4_CR24","doi-asserted-by":"publisher","first-page":"3395","DOI":"10.1016\/j.enpol.2006.11.022","volume":"35","author":"A Tsikalakis","year":"2007","unstructured":"Tsikalakis, A., Hatziargyriou, N.: Environmental benefits of distributed generation with and without emissions trading. Energy Policy 35(6), 3395\u20133409 (2007)","journal-title":"Energy Policy"},{"key":"4_CR25","doi-asserted-by":"crossref","unstructured":"Tun, Y.L., Thar, K., Thwal, C.M., Hong, C.S.: Federated learning based energy demand prediction with clustered aggregation. In: 2021 IEEE International Conference on Big Data and Smart Computing (BigComp). pp. 164\u2013167. IEEE (2021)","DOI":"10.1109\/BigComp51126.2021.00039"},{"issue":"3","key":"4_CR26","doi-asserted-by":"publisher","first-page":"2425","DOI":"10.1109\/TSG.2022.3146489","volume":"13","author":"Y Wang","year":"2022","unstructured":"Wang, Y., Jia, M., Gao, N., Von Krannichfeldt, L., Sun, M., Hug, G.: Federated clustering for electricity consumption pattern extraction. IEEE Transactions on Smart Grid 13(3), 2425\u20132439 (2022)","journal-title":"IEEE Transactions on Smart Grid"},{"key":"4_CR27","doi-asserted-by":"crossref","unstructured":"Zhang, M.G.: Short-term load forecasting based on support vector machines regression. In: 2005 International Conference on Machine Learning and Cybernetics. vol.\u00a07, pp. 4310\u20134314. IEEE (2005)","DOI":"10.1109\/ICMLC.2005.1527695"},{"key":"4_CR28","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Xiao, W., Shuai, L., Luo, J., Yao, S., Zhang, M.: A differential privacy-enhanced federated learning method for short-term household load forecasting in smart grid. In: 2021 7th International Conference on Computer and Communications (ICCC). pp. 1399\u20131404. IEEE (2021)","DOI":"10.1109\/ICCC54389.2021.9674514"}],"container-title":["Lecture Notes in Computer Science","Network and System Security"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-6419-4_4","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,3]],"date-time":"2026-02-03T08:57:55Z","timestamp":1770109075000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-6419-4_4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9789819564187","9789819564194"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-6419-4_4","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":"4 February 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"NSS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Network and System Security","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Wuhan","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":"5 December 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 December 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"nss2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/nsclab.org\/nss-socialsec2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}