{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T13:16:33Z","timestamp":1781788593757,"version":"3.54.5"},"publisher-location":"Cham","reference-count":55,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031371103","type":"print"},{"value":"9783031371110","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-37111-0_13","type":"book-chapter","created":{"date-parts":[[2023,6,28]],"date-time":"2023-06-28T23:04:56Z","timestamp":1687993496000},"page":"175-185","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["BTIMFL: A Blockchain-Based Trust Incentive Mechanism in Federated Learning"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1268-2056","authenticated-orcid":false,"given":"Minjung","family":"Park","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4889-7737","authenticated-orcid":false,"given":"Sangmi","family":"Chai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,6,29]]},"reference":[{"key":"13_CR1","doi-asserted-by":"publisher","first-page":"10700","DOI":"10.1109\/JIOT.2019.2940820","volume":"6","author":"J Kang","year":"2019","unstructured":"Kang, J., Xiong, Z., Niyato, D., Xie, S., Zhang, J.: Incentive mechanism for reliable federated learning: a joint optimization approach to combining reputation and contract theory. IEEE Internet Things J. 6, 10700\u201310714 (2019)","journal-title":"IEEE Internet Things J."},{"key":"13_CR2","doi-asserted-by":"publisher","first-page":"88","DOI":"10.1109\/MCOM.001.1900649","volume":"58","author":"LU Khan","year":"2020","unstructured":"Khan, L.U., et al.: Federated learning for edge networks: resource optimization and incentive mechanism. IEEE Commun. Mag. 58, 88\u201393 (2020)","journal-title":"IEEE Commun. Mag."},{"key":"13_CR3","doi-asserted-by":"publisher","first-page":"6360","DOI":"10.1109\/JIOT.2020.2967772","volume":"7","author":"Y Zhan","year":"2020","unstructured":"Zhan, Y., Li, P., Qu, Z., Zeng, D., Guo, S.: A learning-based incentive mechanism for federated learning. IEEE Internet Things J. 7, 6360\u20136368 (2020)","journal-title":"IEEE Internet Things J."},{"key":"13_CR4","doi-asserted-by":"crossref","unstructured":"Yu, H., et al.: A fairness-aware incentive scheme for federated learning. In: Proceedings of the AAAI\/ACM Conference on AI, Ethics, and Society, pp. 393\u2013399 (2020)","DOI":"10.1145\/3375627.3375840"},{"key":"13_CR5","doi-asserted-by":"crossref","unstructured":"Yan, Y., Ligeti, P.: A survey of personalized and incentive mechanisms for federated learning. In: 2022 IEEE 2nd Conference on Information Technology and Data Science (CITDS), pp. 324\u2013329. IEEE (2022)","DOI":"10.1109\/CITDS54976.2022.9914268"},{"key":"13_CR6","doi-asserted-by":"publisher","unstructured":"Liu, Y., Tian, M., Chen, Y., Xiong, Z., Leung, C., Miao, C.: A contract theory based incentive mechanism for federated learning. Federated and Transfer Learning, pp. 117\u2013137. Springer (2022). https:\/\/doi.org\/10.1007\/978-3-031-11748-0_6","DOI":"10.1007\/978-3-031-11748-0_6"},{"key":"13_CR7","doi-asserted-by":"crossref","unstructured":"Zeng, R., Zhang, S., Wang, J., Chu, X.: Fmore: an incentive scheme of multi-dimensional auction for federated learning in mec. In: 2020 IEEE 40th International Conference on Distributed Computing Systems (ICDCS), pp. 278\u2013288. IEEE (2020)","DOI":"10.1109\/ICDCS47774.2020.00094"},{"key":"13_CR8","doi-asserted-by":"publisher","first-page":"310","DOI":"10.1109\/MNET.011.2000627","volume":"35","author":"Y Zhan","year":"2021","unstructured":"Zhan, Y., Li, P., Guo, S., Qu, Z.: Incentive mechanism design for federated learning: challenges and opportunities. IEEE Network 35, 310\u2013317 (2021)","journal-title":"IEEE Network"},{"key":"13_CR9","doi-asserted-by":"crossref","unstructured":"Shi, Z., et al.: FedFAIM: a model performance-based fair incentive mechanism for federated learning. IEEE Transactions on Big Data (2022)","DOI":"10.1109\/TBDATA.2022.3183614"},{"key":"13_CR10","doi-asserted-by":"publisher","first-page":"229","DOI":"10.1109\/MNET.112.2100706","volume":"36","author":"R Zeng","year":"2022","unstructured":"Zeng, R., Zeng, C., Wang, X., Li, B., Chu, X.: Incentive mechanisms in federated learning and a game-theoretical approach. IEEE Network 36, 229\u2013235 (2022)","journal-title":"IEEE Network"},{"key":"13_CR11","doi-asserted-by":"crossref","unstructured":"Yang, X., Tan, W., Peng, C., Xiang, S., Niu, K.: Federated learning incentive mechanism design via enhanced shapley value method. Wireless Communications and Mobile Computing 2022 (2022)","DOI":"10.1155\/2022\/9690657"},{"key":"13_CR12","doi-asserted-by":"crossref","unstructured":"Yang, C., Liu, J., Sun, H., Li, T., Li, Z.: WTDP-shapley: efficient and effective incentive mechanism in federated learning for intelligent safety inspection. IEEE Transactions on Big Data (2022)","DOI":"10.1109\/TBDATA.2022.3198733"},{"key":"13_CR13","doi-asserted-by":"crossref","unstructured":"Li, L., Yu, X., Cai, X., He, X., Liu, Y.: Contract theory based incentive mechanism for federated learning in health CrowdSensing. IEEE Internet of Things Journal (2022)","DOI":"10.1109\/JIOT.2022.3218008"},{"key":"13_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3570953","volume":"55","author":"J Zhu","year":"2023","unstructured":"Zhu, J., Cao, J., Saxena, D., Jiang, S., Ferradi, H.: Blockchain-empowered federated learning: challenges, solutions, and future directions. ACM Comput. Surv. 55, 1\u201331 (2023)","journal-title":"ACM Comput. Surv."},{"key":"13_CR15","doi-asserted-by":"publisher","first-page":"132","DOI":"10.1016\/j.ins.2022.08.091","volume":"612","author":"T Maumela","year":"2022","unstructured":"Maumela, T., Nelwamondo, F., Marwala, T.: Population based training and federated learning frameworks for hyperparameter optimisation and ML unfairness using ulimisana optimisation algorithm. Inf. Sci. 612, 132\u2013150 (2022)","journal-title":"Inf. Sci."},{"key":"13_CR16","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.: Privacy-preserving federated learning based on multi-key homomorphic encryption. Int. J. Intell. Syst. 37, 5880\u20135901 (2022)","journal-title":"Int. J. Intell. Syst."},{"key":"13_CR17","doi-asserted-by":"crossref","unstructured":"Singh, S.K., Yang, L.T., Park, J.H.: FusionFedBlock: Fusion of blockchain and federated learning to preserve privacy in industry 5.0. Information Fusion 90, 233\u2013240 (2023)","DOI":"10.1016\/j.inffus.2022.09.027"},{"key":"13_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3560816","volume":"55","author":"W Issa","year":"2023","unstructured":"Issa, W., Moustafa, N., Turnbull, B., Sohrabi, N., Tari, Z.: Blockchain-based federated learning for securing internet of things: a comprehensive survey. ACM Comput. Surv. 55, 1\u201343 (2023)","journal-title":"ACM Comput. Surv."},{"key":"13_CR19","first-page":"1035","volume":"10","author":"Y Zhan","year":"2021","unstructured":"Zhan, Y., Zhang, J., Hong, Z., Wu, L., Li, P., Guo, S.: A survey of incentive mechanism design for federated learning. IEEE Trans. Emerg. Top. Comput. 10, 1035\u20131044 (2021)","journal-title":"IEEE Trans. Emerg. Top. Comput."},{"key":"13_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2022.102827","volume":"121","author":"A Qayyum","year":"2022","unstructured":"Qayyum, A., Janjua, M.U., Qadir, J.: Making federated learning robust to adversarial attacks by learning data and model association. Comput. Secur. 121, 102827 (2022)","journal-title":"Comput. Secur."},{"key":"13_CR21","first-page":"814","volume":"4","author":"D Huba","year":"2022","unstructured":"Huba, D., et al.: Papaya: Practical, private, and scalable federated learning. Proc. Machine Learning Syst. 4, 814\u2013832 (2022)","journal-title":"Proc. Machine Learning Syst."},{"key":"13_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.comnet.2021.108691","volume":"204","author":"Z Abubaker","year":"2022","unstructured":"Abubaker, Z., Javaid, N., Almogren, A., Akbar, M., Zuair, M., Ben-Othman, J.: Blockchained service provisioning and malicious node detection via federated learning in scalable Internet of Sensor Things networks. Comput. Netw. 204, 108691 (2022)","journal-title":"Comput. Netw."},{"key":"13_CR23","doi-asserted-by":"publisher","first-page":"8980","DOI":"10.3390\/app12188980","volume":"12","author":"S Pouriyeh","year":"2022","unstructured":"Pouriyeh, S., et al.: Secure smart communication efficiency in federated learning: achievements and challenges. Appl. Sci. 12, 8980 (2022)","journal-title":"Appl. Sci."},{"key":"13_CR24","doi-asserted-by":"crossref","unstructured":"Tu, X., Zhu, K., Luong, N.C., Niyato, D., Zhang, Y., Li, J.: Incentive mechanisms for federated learning: from economic and game theoretic perspective. IEEE Transactions on Cognitive Communications and Networking (2022)","DOI":"10.1109\/TCCN.2022.3177522"},{"key":"13_CR25","doi-asserted-by":"publisher","first-page":"9530","DOI":"10.1109\/JIOT.2020.2991416","volume":"7","author":"R Hu","year":"2020","unstructured":"Hu, R., Guo, Y., Li, H., Pei, Q., Gong, Y.: Personalized federated learning with differential privacy. IEEE Internet Things J. 7, 9530\u20139539 (2020)","journal-title":"IEEE Internet Things J."},{"key":"13_CR26","doi-asserted-by":"crossref","unstructured":"Seif, M., Tandon, R., Li, M.: Wireless federated learning with local differential privacy. In: 2020 IEEE International Symposium on Information Theory (ISIT), pp. 2604\u20132609. IEEE (2020)","DOI":"10.1109\/ISIT44484.2020.9174426"},{"key":"13_CR27","doi-asserted-by":"publisher","first-page":"3454","DOI":"10.1109\/TIFS.2020.2988575","volume":"15","author":"K Wei","year":"2020","unstructured":"Wei, K., et al.: Federated learning with differential privacy: algorithms and performance analysis. IEEE Trans. Inf. Forensics Secur. 15, 3454\u20133469 (2020)","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"13_CR28","doi-asserted-by":"publisher","first-page":"94","DOI":"10.3390\/fi13040094","volume":"13","author":"H Fang","year":"2021","unstructured":"Fang, H., Qian, Q.: Privacy preserving machine learning with homomorphic encryption and federated learning. Future Internet 13, 94 (2021)","journal-title":"Future Internet"},{"key":"13_CR29","unstructured":"Zhang, C., Li, S., Xia, J., Wang, W., Yan, F., Liu, Y.: Batchcrypt: Efficient homomorphic encryption for cross-silo federated learning. In: Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC 2020) (2020)"},{"key":"13_CR30","doi-asserted-by":"crossref","unstructured":"Shahid, O., Pouriyeh, S., Parizi, R.M., Sheng, Q.Z., Srivastava, G., Zhao, L.: Communication efficiency in federated learning: Achievements and challenges. arXiv preprint arXiv:2107.10996 (2021)","DOI":"10.3390\/app12188980"},{"key":"13_CR31","doi-asserted-by":"crossref","unstructured":"Huang, W., Ye, M., Du, B.: Learn from others and be yourself in heterogeneous federated learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10143\u201310153 (2022)","DOI":"10.1109\/CVPR52688.2022.00990"},{"key":"13_CR32","first-page":"50","volume":"37","author":"T Li","year":"2020","unstructured":"Li, T., Sahu, A.K., Talwalkar, A., Smith, V.: Federated learning: challenges, methods, and future directions. IEEE Signal Process. Mag. 37, 50\u201360 (2020)","journal-title":"IEEE Signal Process. Mag."},{"key":"13_CR33","doi-asserted-by":"crossref","unstructured":"Huang, Y., et al.: Personalized cross-silo federated learning on non-iid data. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 7865\u20137873 (2021)","DOI":"10.1609\/aaai.v35i9.16960"},{"key":"13_CR34","first-page":"1","volume":"14","author":"X Zhang","year":"2022","unstructured":"Zhang, X., Gu, H., Fan, L., Chen, K., Yang, Q.: No free lunch theorem for security and utility in federated learning. ACM Trans. Intelligent Syst. Technol. 14, 1\u201335 (2022)","journal-title":"ACM Trans. Intelligent Syst. Technol."},{"key":"13_CR35","doi-asserted-by":"publisher","first-page":"9901","DOI":"10.3390\/app12199901","volume":"12","author":"R Gosselin","year":"2022","unstructured":"Gosselin, R., Vieu, L., Loukil, F., Benoit, A.: Privacy and security in federated learning: a survey. Appl. Sci. 12, 9901 (2022)","journal-title":"Appl. Sci."},{"key":"13_CR36","doi-asserted-by":"publisher","first-page":"148","DOI":"10.1016\/j.inffus.2022.09.011","volume":"90","author":"N Rodr\u00edguez-Barroso","year":"2023","unstructured":"Rodr\u00edguez-Barroso, N., Jim\u00e9nez-L\u00f3pez, D., Luz\u00f3n, M.V., Herrera, F., Mart\u00ednez-C\u00e1mara, E.: Survey on federated learning threats: concepts, taxonomy on attacks and defences, experimental study and challenges. Information Fusion 90, 148\u2013173 (2023)","journal-title":"Information Fusion"},{"key":"13_CR37","doi-asserted-by":"publisher","first-page":"536","DOI":"10.1109\/TPDS.2021.3096076","volume":"33","author":"WYB Lim","year":"2021","unstructured":"Lim, W.Y.B., et al.: Decentralized edge intelligence: a dynamic resource allocation framework for hierarchical federated learning. IEEE Trans. Parallel Distrib. Syst. 33, 536\u2013550 (2021)","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"key":"13_CR38","doi-asserted-by":"crossref","unstructured":"Huang, J., Talbi, R., Zhao, Z., Boucchenak, S., Chen, L.Y., Roos, S.: An exploratory analysis on users\u2019 contributions in federated learning. In: 2020 Second IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA), pp. 20\u201329. IEEE (2020)","DOI":"10.1109\/TPS-ISA50397.2020.00014"},{"key":"13_CR39","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1109\/LNET.2019.2947144","volume":"2","author":"Y Sarikaya","year":"2019","unstructured":"Sarikaya, Y., Ercetin, O.: Motivating workers in federated learning: a stackelberg game perspective. IEEE Networking Letters 2, 23\u201327 (2019)","journal-title":"IEEE Networking Letters"},{"key":"13_CR40","doi-asserted-by":"crossref","unstructured":"Zhao, J., Zhu, X., Wang, J., Xiao, J.: Efficient client contribution evaluation for horizontal federated learning. In: ICASSP 2021\u20132021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 3060\u20133064. IEEE (2021)","DOI":"10.1109\/ICASSP39728.2021.9413377"},{"key":"13_CR41","doi-asserted-by":"crossref","unstructured":"Tang, M., Wong, V.W.: An incentive mechanism for cross-silo federated learning: a public goods perspective. In: IEEE INFOCOM 2021-IEEE Conference on Computer Communications, pp. 1\u201310. IEEE (2021)","DOI":"10.1109\/INFOCOM42981.2021.9488705"},{"key":"13_CR42","doi-asserted-by":"crossref","unstructured":"Wu, H., Tang, X., Zhang, Y.-J.A., Gao, L.: Incentive mechanism for federated learning based on random client sampling. In: 2022 IEEE Globecom Workshops (GC Wkshps), pp. 1640\u20131645. IEEE (2022)","DOI":"10.1109\/GCWkshps56602.2022.10008737"},{"key":"13_CR43","doi-asserted-by":"publisher","first-page":"3123","DOI":"10.1109\/TNSE.2022.3170336","volume":"9","author":"T Mai","year":"2022","unstructured":"Mai, T., Yao, H., Xu, J., Zhang, N., Liu, Q., Guo, S.: Automatic double-auction mechanism for federated learning service market in internet of things. IEEE Trans. Network Science Eng. 9, 3123\u20133135 (2022)","journal-title":"IEEE Trans. Network Science Eng."},{"key":"13_CR44","unstructured":"Karimireddy, S.P., Guo, W., Jordan, M.I.: Mechanisms that Incentivize Data Sharing in Federated Learning. arXiv preprint arXiv:2207.04557 (2022)"},{"key":"13_CR45","doi-asserted-by":"publisher","first-page":"220","DOI":"10.1016\/j.ins.2021.01.004","volume":"557","author":"G Lax","year":"2021","unstructured":"Lax, G., Russo, A., Fasc\u00ec, L.S.: A Blockchain-based approach for matching desired and real privacy settings of social network users. Inf. Sci. 557, 220\u2013235 (2021)","journal-title":"Inf. Sci."},{"key":"13_CR46","doi-asserted-by":"publisher","first-page":"5171","DOI":"10.1109\/JIOT.2020.2977383","volume":"7","author":"Y Qu","year":"2020","unstructured":"Qu, Y., et al.: Decentralized privacy using blockchain-enabled federated learning in fog computing. IEEE Internet Things J. 7, 5171\u20135183 (2020)","journal-title":"IEEE Internet Things J."},{"key":"13_CR47","unstructured":"Mugunthan, V., Rahman, R., Kagal, L.: Blockflow: An Accountable and Privacy-Preserving Solution for Federated Learning. arXiv preprint arXiv:2007.03856 (2020)"},{"key":"13_CR48","doi-asserted-by":"crossref","unstructured":"Han, J., et al.: Tiff: tokenized incentive for federated learning. In: 2022 IEEE 15th International Conference on Cloud Computing (CLOUD), pp. 407\u2013416. IEEE (2022)","DOI":"10.1109\/CLOUD55607.2022.00064"},{"key":"13_CR49","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3538226","volume":"1","author":"M Qi","year":"2022","unstructured":"Qi, M., Wang, Z., Chen, S., Xiang, Y.: A hybrid incentive mechanism for decentralized federated learning. Distributed Ledger Technol.: Res. Practice 1, 1\u201315 (2022)","journal-title":"Distributed Ledger Technol.: Res. Practice"},{"key":"13_CR50","unstructured":"Xu, Y., et al.: Besifl: Blockchain empowered secure and incentive federated learning paradigm in iot. IEEE Internet of Things J. (2021)"},{"key":"13_CR51","doi-asserted-by":"crossref","unstructured":"Short, A.R., Leligou, H.C., Theocharis, E.: Execution of a Federated Learning process within a smart contract. In: 2021 IEEE International Conference on Consumer Electronics (ICCE), pp. 1\u20134. IEEE (2021)","DOI":"10.1109\/ICCE50685.2021.9427734"},{"key":"13_CR52","doi-asserted-by":"publisher","first-page":"176830","DOI":"10.1109\/ACCESS.2020.3021253","volume":"8","author":"G Hua","year":"2020","unstructured":"Hua, G., Zhu, L., Wu, J., Shen, C., Zhou, L., Lin, Q.: Blockchain-based federated learning for intelligent control in heavy haul railway. IEEE Access 8, 176830\u2013176839 (2020)","journal-title":"IEEE Access"},{"key":"13_CR53","first-page":"14","volume":"37","author":"L Cao","year":"2022","unstructured":"Cao, L.: Non-IID federated learning. IEEE Intell. Syst. 37, 14\u201315 (2022)","journal-title":"IEEE Intell. Syst."},{"key":"13_CR54","doi-asserted-by":"crossref","unstructured":"Buyukates, B., et al.: Proof-of-Contribution-Based Design for Collaborative Machine Learning on Blockchain. arXiv preprint arXiv:2302.14031 (2023)","DOI":"10.1109\/DAPPS57946.2023.00012"},{"key":"13_CR55","unstructured":"Rehman, M.H., Salah, K., Damiani, E., Svetinovic, D.: Towards blockchain-based reputation-aware federated learning. In: IEEE INFOCOM 2020-IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), pp. 183\u2013188. IEEE (2020)"}],"container-title":["Lecture Notes in Computer Science","Computational Science and Its Applications \u2013 ICCSA 2023 Workshops"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-37111-0_13","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,16]],"date-time":"2023-12-16T00:17:29Z","timestamp":1702685849000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-37111-0_13"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031371103","9783031371110"],"references-count":55,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-37111-0_13","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"29 June 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICCSA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computational Science and Its Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Athens","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Greece","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3 July 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 July 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iccsa2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/iccsa.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Custom based on Cyberchair 4","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"283","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"67","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"13","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"24% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.5","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"8,5","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"PHD Showcase Papers: 6(for main conf) \/ For ICCSA 2023 Workshops 876 subm sent, 350 full papers and 29 short papers accepted, additional PHD Showcase Papers: 2","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}