{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,25]],"date-time":"2026-01-25T10:01:42Z","timestamp":1769335302680,"version":"3.49.0"},"publisher-location":"Singapore","reference-count":59,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789811980688","type":"print"},{"value":"9789811980695","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-981-19-8069-5_21","type":"book-chapter","created":{"date-parts":[[2022,11,19]],"date-time":"2022-11-19T10:07:42Z","timestamp":1668852462000},"page":"316-331","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Security and\u00a0Privacy Issues and\u00a0Solutions in\u00a0Federated Learning for\u00a0Digital Healthcare"],"prefix":"10.1007","author":[{"given":"Hyejun","family":"Jeong","sequence":"first","affiliation":[]},{"given":"Tai-Myoung","family":"Chung","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,11,20]]},"reference":[{"issue":"6","key":"21_CR1","doi-asserted-by":"publisher","first-page":"4723","DOI":"10.1109\/JIOT.2020.3028742","volume":"8","author":"S AbdulRahman","year":"2020","unstructured":"AbdulRahman, S., Tout, H., Mourad, A., Talhi, C.: FedMCCS: multicriteria client selection model for optimal IoT federated learning. IEEE Internet Things J. 8(6), 4723\u20134735 (2020)","journal-title":"IEEE Internet Things J."},{"key":"21_CR2","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"455","DOI":"10.1007\/978-3-030-88418-5_22","volume-title":"Computer Security","author":"S Awan","year":"2021","unstructured":"Awan, S., Luo, B., Li, F.: CONTRA: defending against poisoning attacks in federated learning. In: Bertino, E., Shulman, H., Waidner, M. (eds.) ESORICS 2021. LNCS, vol. 12972, pp. 455\u2013475. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-88418-5_22"},{"key":"21_CR3","unstructured":"Bagdasaryan, E., Veit, A., Hua, Y., Estrin, D., Shmatikov, V.: How to backdoor federated learning. In: International Conference on Artificial Intelligence and Statistics, pp. 2938\u20132948. PMLR (2020)"},{"key":"21_CR4","unstructured":"Baruch, G., Baruch, M., Goldberg, Y.: A little is enough: circumventing defenses for distributed learning. In: Advances in Neural Information Processing Systems, vol. 32 (2019)"},{"issue":"1","key":"21_CR5","first-page":"49","volume":"36","author":"M Benmalek","year":"2022","unstructured":"Benmalek, M., Benrekia, M.A., Challal, Y.: Security of federated learning: attacks, defensive mechanisms, and challenges. Revue des Sciences et Technologies de l\u2019Information-S\u00e9rie RIA: Revue d\u2019Intelligence Artificielle 36(1), 49\u201359 (2022)","journal-title":"Revue des Sciences et Technologies de l\u2019Information-S\u00e9rie RIA: Revue d\u2019Intelligence Artificielle"},{"key":"21_CR6","unstructured":"Bhagoji, A.N., Chakraborty, S., Mittal, P., Calo, S.: Analyzing federated learning through an adversarial lens. In: International Conference on Machine Learning, pp. 634\u2013643. PMLR (2019)"},{"key":"21_CR7","doi-asserted-by":"crossref","unstructured":"Bhushan, B., Sahoo, G., Rai, A.K.: Man-in-the-middle attack in wireless and computer networking-a review. In: 2017 3rd International Conference on Advances in Computing, Communication & Automation (ICACCA)(Fall), pp. 1\u20136. IEEE (2017)","DOI":"10.1109\/ICACCAF.2017.8344724"},{"key":"21_CR8","unstructured":"Blanchard, P., El Mhamdi, E.M., Guerraoui, R., Stainer, J.: Machine learning with adversaries: byzantine tolerant gradient descent. Adv. Neural Inf. Process. Syst. 30 (2017)"},{"key":"21_CR9","doi-asserted-by":"publisher","first-page":"63229","DOI":"10.1109\/ACCESS.2021.3075203","volume":"9","author":"N Bouacida","year":"2021","unstructured":"Bouacida, N., Mohapatra, P.: Vulnerabilities in federated learning. IEEE Access 9, 63229\u201363249 (2021)","journal-title":"IEEE Access"},{"key":"21_CR10","doi-asserted-by":"crossref","unstructured":"Cao, X., Fang, M., Liu, J., Gong, N.Z.: Fltrust: byzantine-robust federated learning via trust bootstrapping. arXiv preprint arXiv:2012.13995 (2020)","DOI":"10.14722\/ndss.2021.24434"},{"issue":"2","key":"21_CR11","doi-asserted-by":"publisher","first-page":"1070","DOI":"10.1109\/TNSE.2020.3002796","volume":"8","author":"Z Chen","year":"2020","unstructured":"Chen, Z., Tian, P., Liao, W., Yu, W.: Zero knowledge clustering based adversarial mitigation in heterogeneous federated learning. IEEE Trans. Netw. Sci. Eng. 8(2), 1070\u20131083 (2020)","journal-title":"IEEE Trans. Netw. Sci. Eng."},{"issue":"4","key":"21_CR12","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(4), 94 (2021)","journal-title":"Future Internet"},{"key":"21_CR13","unstructured":"Fang, M., Cao, X., Jia, J., Gong, N.: Local model poisoning attacks to $$\\{$$Byzantine-Robust$$\\}$$ federated learning. In: 29th USENIX Security Symposium (USENIX Security 20), pp. 1605\u20131622 (2020)"},{"key":"21_CR14","unstructured":"Fraboni, Y., Vidal, R., Lorenzi, M.: Free-rider attacks on model aggregation in federated learning. In: International Conference on Artificial Intelligence and Statistics, pp. 1846\u20131854. PMLR (2021)"},{"key":"21_CR15","doi-asserted-by":"crossref","unstructured":"Fredrikson, M., Jha, S., Ristenpart, T.: Model inversion attacks that exploit confidence information and basic countermeasures. In: Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications security, pp. 1322\u20131333 (2015)","DOI":"10.1145\/2810103.2813677"},{"key":"21_CR16","unstructured":"Fung, C., Yoon, C.J., Beschastnikh, I.: The limitations of federated learning in sybil settings. In: 23rd International Symposium on Research in Attacks, Intrusions and Defenses (RAID 2020), pp. 301\u2013316 (2020)"},{"key":"21_CR17","first-page":"16937","volume":"33","author":"J Geiping","year":"2020","unstructured":"Geiping, J., Bauermeister, H., Dr\u00f6ge, H., Moeller, M.: Inverting gradients-how easy is it to break privacy in federated learning? Adv. Neural Inf. Process. Syst. 33, 16937\u201316947 (2020)","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"21_CR18","unstructured":"Guerraoui, R., Rouault, S., et al.: The hidden vulnerability of distributed learning in byzantium. In: International Conference on Machine Learning, pp. 3521\u20133530. PMLR (2018)"},{"key":"21_CR19","unstructured":"Haddadpour, F., Kamani, M.M., Mokhtari, A., Mahdavi, M.: Federated learning with compression: unified analysis and sharp guarantees. In: International Conference on Artificial Intelligence and Statistics, pp. 2350\u20132358. PMLR (2021)"},{"issue":"10","key":"21_CR20","doi-asserted-by":"publisher","first-page":"6532","DOI":"10.1109\/TII.2019.2945367","volume":"16","author":"M Hao","year":"2019","unstructured":"Hao, M., Li, H., Luo, X., Xu, G., Yang, H., Liu, S.: Efficient and privacy-enhanced federated learning for industrial artificial intelligence. IEEE Trans. Ind. Inf. 16(10), 6532\u20136542 (2019)","journal-title":"IEEE Trans. Ind. Inf."},{"key":"21_CR21","doi-asserted-by":"publisher","first-page":"103066","DOI":"10.1016\/j.jnca.2021.103066","volume":"185","author":"S Ho","year":"2021","unstructured":"Ho, S., Qu, Y., Gu, B., Gao, L., Li, J., Xiang, Y.: DP-GAN: differentially private consecutive data publishing using generative adversarial nets. J. Netw. Comput. Appl. 185, 103066 (2021)","journal-title":"J. Netw. Comput. Appl."},{"key":"21_CR22","unstructured":"Huang, W., Li, T., Wang, D., Du, S., Zhang, J.: Fairness and accuracy in federated learning. arXiv preprint arXiv:2012.10069 (2020)"},{"key":"21_CR23","unstructured":"Jeong, E., Oh, S., Kim, H., Park, J., Bennis, M., Kim, S.L.: Communication-efficient on-device machine learning: Federated distillation and augmentation under non-iid private data. arXiv preprint arXiv:1811.11479 (2018)"},{"key":"21_CR24","doi-asserted-by":"crossref","unstructured":"Jiang, Y., et al.: Model pruning enables efficient federated learning on edge devices. IEEE Trans. Neural Netw. Learn. Syst. (2022)","DOI":"10.1109\/TNNLS.2022.3166101"},{"key":"21_CR25","unstructured":"Journal, H.: December 2021 healthcare data breach report, June 2022. https:\/\/www.hipaajournal.com\/december-2021-healthcare-data-breach-report\/"},{"key":"21_CR26","doi-asserted-by":"crossref","unstructured":"Kanagavelu, R., et al.: Two-phase multi-party computation enabled privacy-preserving federated learning. In: 2020 20th IEEE\/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGRID), pp. 410\u2013419. IEEE (2020)","DOI":"10.1109\/CCGrid49817.2020.00-52"},{"key":"21_CR27","doi-asserted-by":"publisher","first-page":"357","DOI":"10.1109\/TIFS.2022.3140687","volume":"17","author":"M Khosravy","year":"2022","unstructured":"Khosravy, M., Nakamura, K., Hirose, Y., Nitta, N., Babaguchi, N.: Model inversion attack by integration of deep generative models: Privacy-sensitive face generation from a face recognition system. IEEE Trans. Inf. Forensics Secur. 17, 357\u2013372 (2022). https:\/\/doi.org\/10.1109\/TIFS.2022.3140687","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"21_CR28","unstructured":"Kone\u010dn\u1ef3, J., McMahan, H.B., Yu, F.X., Richt\u00e1rik, P., Suresh, A.T., Bacon, D.: Federated learning: Strategies for improving communication efficiency. arXiv preprint arXiv:1610.05492 (2016)"},{"key":"21_CR29","doi-asserted-by":"crossref","unstructured":"Li, W., Xia, Y., Lu, L., Chen, H., Zang, B.: Teev: virtualizing trusted execution environments on mobile platforms. In: Proceedings of the 15th ACM SIGPLAN\/SIGOPS International Conference on Virtual Execution Environments, pp. 2\u201316 (2019)","DOI":"10.1145\/3313808.3313810"},{"key":"21_CR30","unstructured":"Lin, J., Du, M., Liu, J.: Free-riders in federated learning: attacks and defenses. arXiv preprint arXiv:1911.12560 (2019)"},{"key":"21_CR31","doi-asserted-by":"crossref","unstructured":"Lu, Y., Fan, L.: An efficient and robust aggregation algorithm for learning federated CNN. In: Proceedings of the 2020 3rd International Conference on Signal Processing and Machine Learning, pp. 1\u20137 (2020)","DOI":"10.1145\/3432291.3432303"},{"key":"21_CR32","unstructured":"Lyu, L., et al.: Privacy and robustness in federated learning: attacks and defenses. arXiv preprint arXiv:2012.06337 (2020)"},{"key":"21_CR33","doi-asserted-by":"crossref","unstructured":"Lyu, L., Yu, H., Yang, Q.: Threats to federated learning: a survey. arXiv preprint arXiv:2003.02133 (2020)","DOI":"10.1007\/978-3-030-63076-8_1"},{"key":"21_CR34","doi-asserted-by":"crossref","unstructured":"Ma, Y., Zhu, X., Hsu, J.: Data poisoning against differentially-private learners: attacks and defenses. arXiv preprint arXiv:1903.09860 (2019)","DOI":"10.24963\/ijcai.2019\/657"},{"key":"21_CR35","doi-asserted-by":"crossref","unstructured":"Mallik, A.: Man-in-the-middle-attack: understanding in simple words. Cyberspace: Jurnal Pendidikan Teknologi Informasi 2(2), 109\u2013134 (2019)","DOI":"10.22373\/cj.v2i2.3453"},{"key":"21_CR36","doi-asserted-by":"crossref","unstructured":"Melis, L., Song, C., De Cristofaro, 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"},{"key":"21_CR37","doi-asserted-by":"crossref","unstructured":"Miao, L., Yang, W., Hu, R., Li, L., Huang, L.: Against backdoor attacks in federated learning with differential privacy. In: ICASSP 2022\u20132022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2999\u20133003. IEEE (2022)","DOI":"10.1109\/ICASSP43922.2022.9747653"},{"key":"21_CR38","unstructured":"Mo, F., Haddadi, H.: Efficient and private federated learning using tee. In: Proceedings of EuroSys Conference on, Dresden, Germany (2019)"},{"key":"21_CR39","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. 94\u2013108 (2021)","DOI":"10.1145\/3458864.3466628"},{"key":"21_CR40","doi-asserted-by":"publisher","first-page":"619","DOI":"10.1016\/j.future.2020.10.007","volume":"115","author":"V Mothukuri","year":"2021","unstructured":"Mothukuri, V., Parizi, R.M., Pouriyeh, S., Huang, Y., Dehghantanha, A., Srivastava, G.: A survey on security and privacy of federated learning. Future Gener. Comput. Syst. 115, 619\u2013640 (2021)","journal-title":"Future Gener. Comput. Syst."},{"key":"21_CR41","doi-asserted-by":"crossref","unstructured":"Nasr, M., Shokri, R., Houmansadr, A.: Comprehensive privacy analysis of deep learning: passive and active white-box inference attacks against centralized and federated learning. In: 2019 IEEE Symposium on Security and Privacy (SP), pp. 739\u2013753. IEEE (2019)","DOI":"10.1109\/SP.2019.00065"},{"key":"21_CR42","unstructured":"Nguyen, T.D., et al.: Flame: taming backdoors in federated learning. Cryptology ePrint Archive (2021)"},{"key":"21_CR43","doi-asserted-by":"crossref","unstructured":"Nishio, T., Yonetani, R.: Client selection for federated learning with heterogeneous resources in mobile edge. In: ICC 2019\u20132019 IEEE International Conference on Communications (ICC), pp. 1\u20137. IEEE (2019)","DOI":"10.1109\/ICC.2019.8761315"},{"key":"21_CR44","doi-asserted-by":"crossref","unstructured":"Pan, X., Zhang, M., Ji, S., Yang, M.: Privacy risks of general-purpose language models. In: 2020 IEEE Symposium on Security and Privacy (SP), pp. 1314\u20131331. IEEE (2020)","DOI":"10.1109\/SP40000.2020.00095"},{"key":"21_CR45","doi-asserted-by":"crossref","unstructured":"Parisot, M.P.M., Pejo, B., Spagnuelo, D.: Property inference attacks on convolutional neural networks: influence and implications of target model\u2019s complexity. CoRR abs\/2104.13061 (2021). https:\/\/arxiv.org\/abs\/2104.13061","DOI":"10.5220\/0010555607150721"},{"key":"21_CR46","doi-asserted-by":"crossref","unstructured":"Shejwalkar, V., Houmansadr, A.: Manipulating the byzantine: optimizing model poisoning attacks and defenses for federated learning. In: NDSS (2021)","DOI":"10.14722\/ndss.2021.24498"},{"key":"21_CR47","doi-asserted-by":"publisher","unstructured":"Shokri, R., Stronati, M., Song, C., Shmatikov, V.: Membership inference attacks against machine learning models. In: 2017 IEEE Symposium on Security and Privacy (SP), pp. 3\u201318 (2017). https:\/\/doi.org\/10.1109\/SP.2017.41","DOI":"10.1109\/SP.2017.41"},{"key":"21_CR48","unstructured":"Sun, Z., Kairouz, P., Suresh, A.T., McMahan, H.B.: Can you really backdoor federated learning? arXiv preprint arXiv:1911.07963 (2019)"},{"key":"21_CR49","doi-asserted-by":"crossref","unstructured":"Truex, S., et al.: A hybrid approach to privacy-preserving federated learning. In: Proceedings of the 12th ACM Workshop on Artificial Intelligence and Security, pp. 1\u201311 (2019)","DOI":"10.1145\/3338501.3357370"},{"key":"21_CR50","unstructured":"Wang, H., et al.: Attack of the tails: yes, you really can backdoor federated learning. Adv. Neural Inf. Process. Syst. 33, 16070\u201316084 (2020)"},{"key":"21_CR51","doi-asserted-by":"crossref","unstructured":"Wang, N., Xiao, Y., Chen, Y., Hu, Y., Lou, W., Hou, Y.T.: Flare: defending federated learning against model poisoning attacks via latent space representations. In: Proceedings of the 2022 ACM on Asia Conference on Computer and Communications Security, pp. 946\u2013958 (2022)","DOI":"10.1145\/3488932.3517395"},{"key":"21_CR52","unstructured":"Wu, C., Yang, X., Zhu, S., Mitra, P.: Mitigating backdoor attacks in federated learning. arXiv preprint arXiv:2011.01767 (2020)"},{"issue":"1","key":"21_CR53","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41467-021-27699-2","volume":"13","author":"C Wu","year":"2022","unstructured":"Wu, C., Wu, F., Lyu, L., Huang, Y., Xie, X.: Communication-efficient federated learning via knowledge distillation. Nature Commun. 13(1), 1\u20138 (2022)","journal-title":"Nature Commun."},{"key":"21_CR54","unstructured":"Xie, C., Huang, K., Chen, P.Y., Li, B.: Dba: Distributed backdoor attacks against federated learning. In: International Conference on Learning Representations (2019)"},{"key":"21_CR55","unstructured":"Yin, D., Chen, Y., Kannan, R., Bartlett, P.: Byzantine-robust distributed learning: towards optimal statistical rates. In: International Conference on Machine Learning, pp. 5650\u20135659. PMLR (2018)"},{"key":"21_CR56","unstructured":"Zhang, C., Li, S., Xia, J., Wang, W., Yan, F., Liu, Y.: $$\\{$$BatchCrypt$$\\}$$: Efficient homomorphic encryption for $$\\{$$Cross-Silo$$\\}$$ federated learning. In: 2020 USENIX Annual Technical Conference (USENIX ATC 20), pp. 493\u2013506 (2020)"},{"key":"21_CR57","doi-asserted-by":"crossref","unstructured":"Zhang, L., Shen, L., Ding, L., Tao, D., Duan, L.Y.: Fine-tuning global model via data-free knowledge distillation for non-iid federated learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10174\u201310183 (2022)","DOI":"10.1109\/CVPR52688.2022.00993"},{"key":"21_CR58","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Jia, R., Pei, H., Wang, W., Li, B., Song, D.: The secret revealer: generative model-inversion attacks against deep neural networks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 253\u2013261 (2020)","DOI":"10.1109\/CVPR42600.2020.00033"},{"key":"21_CR59","doi-asserted-by":"crossref","unstructured":"Zhou, Z., Xu, C., Wang, M., Ma, T., Yu, S.: Augmented dual-shuffle-based moving target defense to ensure CIA-triad in federated learning. In: 2021 IEEE Global Communications Conference (GLOBECOM), pp. 01\u201306. IEEE (2021)","DOI":"10.1109\/GLOBECOM46510.2021.9685154"}],"container-title":["Communications in Computer and Information Science","Future Data and Security Engineering. Big Data, Security and Privacy, Smart City and Industry 4.0 Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-19-8069-5_21","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,19]],"date-time":"2022-11-19T10:11:57Z","timestamp":1668852717000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-19-8069-5_21"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9789811980688","9789811980695"],"references-count":59,"URL":"https:\/\/doi.org\/10.1007\/978-981-19-8069-5_21","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"20 November 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"FDSE","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Future Data and Security Engineering","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Ho Chi Minh City","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Vietnam","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 November 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25 November 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"fdse2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/thefdse.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":"Easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"170","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":"41","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":"12","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":"3","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":"6","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":"4 full papers from invited keynote speakers","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)"}}]}}