{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T00:14:08Z","timestamp":1769732048774,"version":"3.49.0"},"reference-count":49,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T00:00:00Z","timestamp":1759276800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T00:00:00Z","timestamp":1759276800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"electric power project","award":["360022JX0120240081"],"award-info":[{"award-number":["360022JX0120240081"]}]},{"name":"electric power project","award":["360022JX0120240081"],"award-info":[{"award-number":["360022JX0120240081"]}]},{"name":"electric power project","award":["360022JX0120240081"],"award-info":[{"award-number":["360022JX0120240081"]}]},{"name":"electric power project","award":["360022JX0120240081"],"award-info":[{"award-number":["360022JX0120240081"]}]},{"name":"electric power project","award":["360022JX0120240081"],"award-info":[{"award-number":["360022JX0120240081"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Peer-to-Peer Netw. Appl."],"published-print":{"date-parts":[[2025,10]]},"DOI":"10.1007\/s12083-025-02116-3","type":"journal-article","created":{"date-parts":[[2025,10,7]],"date-time":"2025-10-07T04:27:52Z","timestamp":1759811272000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Privacy-preserving federated learning scheme for distributed smart grid based on multi-key homomorphic encryption"],"prefix":"10.1007","volume":"18","author":[{"given":"Penglin","family":"Zhang","sequence":"first","affiliation":[]},{"given":"Yong","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Zhaodong","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Qiuyao","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Zhenghua","family":"Gu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,10,7]]},"reference":[{"key":"2116_CR1","doi-asserted-by":"publisher","first-page":"1009","DOI":"10.1016\/j.ijhydene.2024.01.129","volume":"67","author":"M SaberiKamarposhti","year":"2024","unstructured":"SaberiKamarposhti M, Kamyab H, Krishnan S, Yusuf M, Rezania S, Chelliapan S, Khorami M (2024) A comprehensive review of ai-enhanced smart grid integration for hydrogen energy: Advances, challenges, and future prospects. Int J Hydrogen Energy 67:1009\u20131025","journal-title":"Int J Hydrogen Energy"},{"key":"2116_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2023.129661","volume":"287","author":"M Fotopoulou","year":"2024","unstructured":"Fotopoulou M, Rakopoulos D, Petridis S, Drosatos P (2024) Assessment of smart grid operation under emergency situations. Energy 287:129661","journal-title":"Energy"},{"key":"2116_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.renene.2023.119661","volume":"220","author":"L Nazir","year":"2024","unstructured":"Nazir L, Sharifi A (2024) An analysis of barriers to the implementation of smart grid technology in pakistan. Renewable Energy 220:119661","journal-title":"Renewable Energy"},{"key":"2116_CR4","doi-asserted-by":"crossref","unstructured":"Lei J, Wang L, Pei Q, Sun W, Lin X, Liu X (2024) Privgrid: Privacy-preserving individual load forecasting service for smart grid. IEEE Transactions on Information Forensics and Security","DOI":"10.1109\/TIFS.2024.3422876"},{"key":"2116_CR5","doi-asserted-by":"crossref","unstructured":"Ibrahem MI, Fouda MM (2024) A lightweight privacy-preserving load forecasting and monitoring scheme supporting dynamic billing for smart grids: No kdc required. IEEE Internet of Things Journal","DOI":"10.1109\/JIOT.2024.3426486"},{"key":"2116_CR6","doi-asserted-by":"publisher","first-page":"304","DOI":"10.1016\/j.future.2020.09.019","volume":"115","author":"Y Ren","year":"2021","unstructured":"Ren Y, Leng Y, Qi J, Sharma PK, Wang J, Almakhadmeh Z, Tolba A (2021) Multiple cloud storage mechanism based on blockchain in smart homes. Futur Gener Comput Syst 115:304\u2013313","journal-title":"Futur Gener Comput Syst"},{"key":"2116_CR7","doi-asserted-by":"crossref","unstructured":"Chen D, Liao Z, Xie Z, Chen R, Qin Z, Cao M, Dai H-N, Zhang K (2024) Mfsse: multi-keyword fuzzy ranked symmetric searchable encryption with pattern hidden in mobile cloud computing. IEEE Transactions on Cloud Computing","DOI":"10.1109\/TCC.2024.3430237"},{"issue":"7","key":"2116_CR8","doi-asserted-by":"publisher","first-page":"478","DOI":"10.1038\/s44287-024-00053-6","volume":"1","author":"L Ale","year":"2024","unstructured":"Ale L, Zhang N, King SA, Chen D (2024) Empowering generative ai through mobile edge computing. Nature Rev Electr Eng 1(7):478\u2013486","journal-title":"Nature Rev Electr Eng"},{"key":"2116_CR9","doi-asserted-by":"crossref","unstructured":"Su H-Y, Lai C-C (2024) Towards improved load forecasting in smart grids: A robust deep ensemble learning framework. IEEE Transactions on Smart Grid","DOI":"10.1109\/TSG.2024.3402011"},{"issue":"1","key":"2116_CR10","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1186\/s40537-024-00909-6","volume":"11","author":"H Ali El-Sayed Ali","year":"2024","unstructured":"Ali El-Sayed Ali H, Alham M, Ibrahim DK (2024) Big data resolving using apache spark for load forecasting and demand response in smart grid: a case study of low carbon london project. Journal of Big Data 11(1):59","journal-title":"Journal of Big Data"},{"issue":"9","key":"2116_CR11","doi-asserted-by":"publisher","first-page":"16398","DOI":"10.1109\/JIOT.2024.3354045","volume":"11","author":"N Abdi","year":"2024","unstructured":"Abdi N, Albaseer A, Abdallah M (2024) The role of deep learning in advancing proactive cybersecurity measures for smart grid networks: A survey. IEEE Internet of Things Journal 11(9):16398\u201316421","journal-title":"IEEE Internet of Things Journal"},{"key":"2116_CR12","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1016\/j.aej.2024.05.085","volume":"102","author":"S Rani","year":"2024","unstructured":"Rani S, Shabaz M, Dutta AK, Ahmed EA et al (2024) Enhancing privacy and security in iot-based smart grid system using encryption-based fog computing. Alex Eng J 102:66\u201374","journal-title":"Alex Eng J"},{"issue":"1","key":"2116_CR13","doi-asserted-by":"publisher","first-page":"467","DOI":"10.1109\/TII.2020.3045161","volume":"18","author":"D Chen","year":"2020","unstructured":"Chen D, Zhao Z, Qin X, Luo Y, Cao M, Xu H, Liu A (2020) Magleak: A learning-based side-channel attack for password recognition with multiple sensors in iiot environment. IEEE Trans Ind Inf 18(1):467\u2013476","journal-title":"IEEE Trans Ind Inf"},{"key":"2116_CR14","doi-asserted-by":"publisher","first-page":"102576","DOI":"10.1016\/j.inffus.2024.102576","volume":"112","author":"W Huang","year":"2024","unstructured":"Huang W, Wang D, Ouyang X, Wan J, Liu J, Li T (2024) Multimodal federated learning: Concept, methods, applications and future directions. Inf Fusion 112:102576","journal-title":"Inf Fusion"},{"issue":"6","key":"2116_CR15","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 (2021) A comprehensive survey of privacy-preserving federated learning: A taxonomy, review, and future directions. ACM Comput Surv (CSUR) 54(6):1\u201336","journal-title":"ACM Comput Surv (CSUR)"},{"key":"2116_CR16","unstructured":"Kone\u010dn\u1ef3 J, McMahan HB, Yu FX, Richt\u00e1rik P, Suresh AT, Bacon D (2016) Federated learning: Strategies for improving communication efficiency. arXiv:1610.05492"},{"issue":"2","key":"2116_CR17","doi-asserted-by":"publisher","first-page":"1333","DOI":"10.1109\/TII.2021.3095506","volume":"18","author":"Z Su","year":"2021","unstructured":"Su Z, Wang Y, Luan TH, Zhang N, Li F, Chen T, Cao H (2021) Secure and efficient federated learning for smart grid with edge-cloud collaboration. IEEE Trans Ind Inf 18(2):1333\u20131344","journal-title":"IEEE Trans Ind Inf"},{"issue":"6","key":"2116_CR18","doi-asserted-by":"publisher","first-page":"4862","DOI":"10.1109\/TSG.2022.3204796","volume":"13","author":"Y Li","year":"2022","unstructured":"Li Y, Wei X, Li Y, Dong Z, Shahidehpour M (2022) Detection of false data injection attacks in smart grid: A secure federated deep learning approach. IEEE Transactions on Smart Grid 13(6):4862\u20134872","journal-title":"IEEE Transactions on Smart Grid"},{"key":"2116_CR19","doi-asserted-by":"crossref","unstructured":"Rajesh M, Ramachandran S, Vengatesan K, Dhanabalan SS, Nataraj SK (2024) Federated learning for personalized recommendation in securing power traces in smart grid systems. IEEE T. Consum, Electr","DOI":"10.1109\/TCE.2024.3368087"},{"key":"2116_CR20","doi-asserted-by":"publisher","first-page":"109848","DOI":"10.1016\/j.ijepes.2024.109848","volume":"157","author":"J Wang","year":"2024","unstructured":"Wang J, Si Y, Zhu Y, Zhang K, Yin S, Liu B (2024) Cyberattack detection for electricity theft in smart grids via stacking ensemble gru optimization algorithm using federated learning framework. Int J Electr Power & Energy Syst 157:109848","journal-title":"Int J Electr Power & Energy Syst"},{"issue":"5","key":"2116_CR21","first-page":"1333","volume":"13","author":"Y Aono","year":"2017","unstructured":"Aono Y, Hayashi T, Wang L, Moriai S et al (2017) Privacy-preserving deep learning via additively homomorphic encryption. IEEE Trans Inf Forensics Secur 13(5):1333\u20131345","journal-title":"IEEE Trans Inf Forensics Secur"},{"key":"2116_CR22","doi-asserted-by":"publisher","first-page":"3454","DOI":"10.1109\/TIFS.2020.2988575","volume":"15","author":"K Wei","year":"2020","unstructured":"Wei K, Li J, Ding M, Ma C, Yang HH, Farokhi F, Jin S, Quek TQ, Poor HV (2020) Federated learning with differential privacy: Algorithms and performance analysis. IEEE Trans Inf Forensics Secur 15:3454\u20133469","journal-title":"IEEE Trans Inf Forensics Secur"},{"key":"2116_CR23","doi-asserted-by":"crossref","unstructured":"Truex S, Liu L, Chow K-H, Gursoy ME, Wei W (2020) Ldp-fed: Federated learning with local differential privacy. In: Proceedings of the Third ACM international workshop on edge systems, analytics and networking, pp 61\u201366","DOI":"10.1145\/3378679.3394533"},{"issue":"1","key":"2116_CR24","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1109\/JIOT.2023.3299947","volume":"11","author":"Z He","year":"2023","unstructured":"He Z, Wang L, Cai Z (2023) Clustered federated learning with adaptive local differential privacy on heterogeneous iot data. IEEE Internet of Things Journal 11(1):137\u2013146","journal-title":"IEEE Internet of Things Journal"},{"key":"2116_CR25","doi-asserted-by":"crossref","unstructured":"Zhang C, Liu Z, Xu X, Hu F, Dai J, Cai B, Wang W (2025) Sensfl: Privacy-preserving vertical federated learning with sensitive regularization. CMES-Computer Modeling in Engineering & Sciences 142(1)","DOI":"10.32604\/cmes.2024.055596"},{"issue":"4","key":"2116_CR26","doi-asserted-by":"publisher","first-page":"5578","DOI":"10.1109\/JIOT.2023.3308170","volume":"11","author":"C Ren","year":"2023","unstructured":"Ren C, Yu H, Yan R, Li Q, Xu Y, Niyato D, Dong ZY (2023) Secfedsa: A secure differential-privacy-based federated learning approach for smart cyber-physical grid stability assessment. IEEE Internet of Things Journal 11(4):5578\u20135588","journal-title":"IEEE Internet of Things Journal"},{"issue":"8","key":"2116_CR27","doi-asserted-by":"publisher","first-page":"6069","DOI":"10.1109\/JIOT.2021.3110784","volume":"9","author":"M Wen","year":"2021","unstructured":"Wen M, Xie R, Lu K, Wang L, Zhang K (2021) Feddetect: A novel privacy-preserving federated learning framework for energy theft detection in smart grid. IEEE Internet of Things Journal 9(8):6069\u20136080","journal-title":"IEEE Internet of Things Journal"},{"key":"2116_CR28","first-page":"4961","volume":"34","author":"B Knott","year":"2021","unstructured":"Knott B, Venkataraman S, Hannun A, Sengupta S, Ibrahim M, Maaten L (2021) Crypten: Secure multi-party computation meets machine learning. Adv Neural Inf Process Syst 34:4961\u20134973","journal-title":"Adv Neural Inf Process Syst"},{"key":"2116_CR29","doi-asserted-by":"crossref","unstructured":"Gehlhar T, Marx F, Schneider T, Suresh A, Wehrle T, Yalame H (2023) Safefl: Mpc-friendly framework for private and robust federated learning. In: 2023 IEEE Security and Privacy Workshops (SPW) pp 69\u201376. IEEE","DOI":"10.1109\/SPW59333.2023.00012"},{"key":"2116_CR30","doi-asserted-by":"crossref","unstructured":"Chen L, Xiao D, Xiao X, Zhang Y (2024) Secure and efficient federated learning via novel authenticable multi-party computation and compressed sensing. IEEE Transactions on Information Forensics and Security","DOI":"10.1016\/j.ins.2024.120481"},{"key":"2116_CR31","doi-asserted-by":"crossref","unstructured":"Guo H, Mao Y, He X, Zhang B, Pang T, Ping P (2024) Improving federated learning through abnormal client detection and incentive. CMES-Computer Modeling in Engineering & Sciences 139(1)","DOI":"10.32604\/cmes.2023.031466"},{"issue":"7","key":"2116_CR32","doi-asserted-by":"publisher","first-page":"170305","DOI":"10.1007\/s11432-023-3978-y","volume":"67","author":"L Sun","year":"2024","unstructured":"Sun L, Wang Y, Ren Y, Xia F (2024) Path signature-based xai-enabled network time series classification. Sci China Inf Sci 67(7):170305","journal-title":"Sci China Inf Sci"},{"key":"2116_CR33","unstructured":"Zhang C, Li S, Xia J, Wang W, Yan F, Liu Y (2020) $$\\{$$BatchCrypt$$\\}$$: Efficient homomorphic encryption for $$\\{$$Cross-Silo$$\\}$$ federated learning. In: 2020 USENIX Annual Technical Conference (USENIX ATC 20) pp 493\u2013506"},{"issue":"13","key":"2116_CR34","doi-asserted-by":"publisher","first-page":"11590","DOI":"10.1109\/JIOT.2021.3130115","volume":"9","author":"Y Li","year":"2021","unstructured":"Li Y, Li H, Xu G, Huang X, Lu R (2021) Efficient privacy-preserving federated learning with unreliable users. IEEE Internet of Things Journal 9(13):11590\u201311603","journal-title":"IEEE Internet of Things Journal"},{"issue":"20","key":"2116_CR35","doi-asserted-by":"publisher","first-page":"20149","DOI":"10.1109\/JIOT.2022.3171767","volume":"9","author":"C He","year":"2022","unstructured":"He C, Liu G, Guo S, Yang Y (2022) Privacy-preserving and low-latency federated learning in edge computing. IEEE Internet of Things Journal 9(20):20149\u201320159","journal-title":"IEEE Internet of Things Journal"},{"issue":"4","key":"2116_CR36","doi-asserted-by":"publisher","first-page":"1874","DOI":"10.3934\/mbe.2019091","volume":"16","author":"Y Ren","year":"2019","unstructured":"Ren Y, Leng Y, Cheng Y, Wang J (2019) Secure data storage based on blockchain and coding in edge computing. Math Biosci Eng 16(4):1874\u20131892","journal-title":"Math Biosci Eng"},{"issue":"9","key":"2116_CR37","doi-asserted-by":"publisher","first-page":"5880","DOI":"10.1002\/int.22818","volume":"37","author":"J Ma","year":"2022","unstructured":"Ma J, Naas S-A, Sigg S, Lyu X (2022) Privacy-preserving federated learning based on multi-key homomorphic encryption. Int J Intell Syst 37(9):5880\u20135901","journal-title":"Int J Intell Syst"},{"issue":"4","key":"2116_CR38","doi-asserted-by":"publisher","first-page":"48","DOI":"10.3390\/cryptography7040048","volume":"7","author":"I Walskaar","year":"2023","unstructured":"Walskaar I, Tran MC, Catak FO (2023) A practical implementation of medical privacy-preserving federated learning using multi-key homomorphic encryption and flower framework. Cryptography 7(4):48","journal-title":"Cryptography"},{"issue":"5","key":"2116_CR39","doi-asserted-by":"publisher","first-page":"4923","DOI":"10.1007\/s40747-023-00978-9","volume":"9","author":"W Du","year":"2023","unstructured":"Du W, Li M, Wu L, Han Y, Zhou T, Yang X (2023) A efficient and robust privacy-preserving framework for cross-device federated learning. Complex & Intell Syst 9(5):4923\u20134937","journal-title":"Complex & Intell Syst"},{"issue":"4","key":"2116_CR40","doi-asserted-by":"publisher","first-page":"3817","DOI":"10.1109\/TDSC.2023.3336977","volume":"21","author":"Y Cai","year":"2023","unstructured":"Cai Y, Ding W, Xiao Y, Yan Z, Liu X, Wan Z (2023) Secfed: A secure and efficient federated learning based on multi-key homomorphic encryption. IEEE T Depend Secure 21(4):3817\u20133833","journal-title":"IEEE T Depend Secure"},{"key":"2116_CR41","doi-asserted-by":"crossref","unstructured":"Chen H, Dai W, Kim M, Song Y (2019) Efficient multi-key homomorphic encryption with packed ciphertexts with application to oblivious neural network inference. In: Proceedings of the 2019 ACM SIGSAC conference on computer and communications security, pp 395\u2013412","DOI":"10.1145\/3319535.3363207"},{"key":"2116_CR42","unstructured":"Alkim E, Ducas L, P\u00f6ppelmann T, Schwabe P (2016) Post-quantum key $$\\{$$Exchange\u2014A$$\\}$$ new hope. In: 25th USENIX Security Symposium (USENIX Security 16) pp 327\u2013343"},{"issue":"12","key":"2116_CR43","first-page":"3268","volume":"71","author":"C Liu","year":"2022","unstructured":"Liu C, Guo H, Xu M, Wang S, Yu D, Yu J, Cheng X (2022) Extending on-chain trust to off-chain-trustworthy blockchain data collection using trusted execution environment (tee). IEEE T Comput 71(12):3268\u20133280","journal-title":"IEEE T Comput"},{"issue":"3","key":"2116_CR44","doi-asserted-by":"publisher","first-page":"551","DOI":"10.1109\/TPDS.2021.3097277","volume":"33","author":"Y Gao","year":"2021","unstructured":"Gao Y, Xu J, Wang H (2021) cunh: Efficient gpu implementations of post-quantum kem newhope. IEEE T Parall Distr 33(3):551\u2013568","journal-title":"IEEE T Parall Distr"},{"issue":"2","key":"2116_CR45","doi-asserted-by":"publisher","first-page":"1703","DOI":"10.1109\/TII.2022.3170348","volume":"19","author":"AP Kalapaaking","year":"2022","unstructured":"Kalapaaking AP, Khalil I, Rahman MS, Atiquzzaman M, Yi X, Almashor M (2022) Blockchain-based federated learning with secure aggregation in trusted execution environment for internet-of-things. IEEE Trans Ind Inf 19(2):1703\u20131714","journal-title":"IEEE Trans Ind Inf"},{"key":"2116_CR46","doi-asserted-by":"crossref","unstructured":"Wu P, Ning J, Shen J, Wang H, Chang E-C (2022) Hybrid trust multi-party computation with trusted execution environment. In: NDSS","DOI":"10.14722\/ndss.2022.24173"},{"issue":"7","key":"2116_CR47","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3594542","volume":"20","author":"Y Ren","year":"2024","unstructured":"Ren Y, Lv Z, Xiong NN, Wang J (2024) Hcnct: A cross-chain interaction scheme for the blockchain-based metaverse. ACM Trans Multimed Comput, Commun Appl 20(7):1\u201323","journal-title":"ACM Trans Multimed Comput, Commun Appl"},{"issue":"6","key":"2116_CR48","doi-asserted-by":"publisher","first-page":"1923","DOI":"10.1109\/TPDS.2023.3267897","volume":"34","author":"Q Chen","year":"2023","unstructured":"Chen Q, Wang Z, Chen J, Yan H, Lin X (2023) Dap-fl: Federated learning flourishes by adaptive tuning and secure aggregation. IEEE T Parall Distr 34(6):1923\u20131941","journal-title":"IEEE T Parall Distr"},{"key":"2116_CR49","unstructured":"Zhang C, Li S, Xia J, Wang W, Yan F, Liu Y (2020) $$\\{$$BatchCrypt$$\\}$$: Efficient homomorphic encryption for $$\\{$$Cross-Silo$$\\}$$ federated learning. In: 2020 USENIX annual technical conference (USENIX ATC 20), pp 493\u2013506"}],"container-title":["Peer-to-Peer Networking and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12083-025-02116-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12083-025-02116-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12083-025-02116-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,13]],"date-time":"2025-12-13T10:37:59Z","timestamp":1765622279000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12083-025-02116-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10]]},"references-count":49,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2025,10]]}},"alternative-id":["2116"],"URL":"https:\/\/doi.org\/10.1007\/s12083-025-02116-3","relation":{},"ISSN":["1936-6442","1936-6450"],"issn-type":[{"value":"1936-6442","type":"print"},{"value":"1936-6450","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10]]},"assertion":[{"value":"24 April 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 August 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 October 2025","order":3,"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":"Competing interests"}}],"article-number":"293"}}