{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T23:36:07Z","timestamp":1742945767084,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":20,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819712731"},{"type":"electronic","value":"9789819712748"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-981-97-1274-8_13","type":"book-chapter","created":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T19:20:02Z","timestamp":1710271202000},"page":"191-204","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Poison Egg: Scrambling Federated Learning with\u00a0Delayed Backdoor Attack"],"prefix":"10.1007","author":[{"given":"Masayoshi","family":"Tsutsui","sequence":"first","affiliation":[]},{"given":"Tatsuya","family":"Kaneko","sequence":"additional","affiliation":[]},{"given":"Shinya","family":"Takamaeda-Yamazaki","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,3,13]]},"reference":[{"key":"13_CR1","doi-asserted-by":"crossref","unstructured":"Andreina, S., Marson, G.A., M\u00f6llering, H., Karame, G.: Baffle: backdoor detection via feedback-based federated learning. In: 2021 IEEE 41st International Conference on Distributed Computing Systems (ICDCS), pp. 852\u2013863. IEEE (2021)","DOI":"10.1109\/ICDCS51616.2021.00086"},{"key":"13_CR2","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"455","DOI":"10.1007\/978-3-030-88418-5_22","volume-title":"Computer Security-ESORICS 2021","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 Part I 26. LNCS, vol. 12972, pp. 455\u2013475. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-88418-5_22"},{"key":"13_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":"13_CR4","unstructured":"Balunovi\u0107, M., Dimitrov, D.I., Jovanovi\u0107, N., Vechev, M.: Lamp: extracting text from gradients with language model priors (2022)"},{"key":"13_CR5","doi-asserted-by":"crossref","unstructured":"Breunig, M.M., Kriegel, H.P., Ng, R.T., Sander, J.: Lof: identifying density-based local outliers. In: Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, pp. 93\u2013104 (2000)","DOI":"10.1145\/342009.335388"},{"key":"13_CR6","unstructured":"Chen, X., Liu, C., Li, B., Lu, K., Song, D.: Targeted backdoor attacks on deep learning systems using data poisoning. arXiv preprint arXiv:1712.05526 (2017)"},{"key":"13_CR7","doi-asserted-by":"crossref","unstructured":"Deng, J., et al.: Tag: gradient attack on transformer-based language models. arXiv preprint arXiv:2103.06819 (2021)","DOI":"10.18653\/v1\/2021.findings-emnlp.305"},{"key":"13_CR8","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"13_CR9","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1016\/j.compag.2018.02.016","volume":"147","author":"A Kamilaris","year":"2018","unstructured":"Kamilaris, A., Prenafeta-Bold\u00fa, F.X.: Deep learning in agriculture: a survey. Comput. Electron. Agric. 147, 70\u201390 (2018)","journal-title":"Comput. Electron. Agric."},{"key":"13_CR10","unstructured":"Krizhevsky, A., Hinton, G., et al.: Learning multiple layers of features from tiny images (2009)"},{"key":"13_CR11","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","volume":"42","author":"G Litjens","year":"2017","unstructured":"Litjens, G., et al.: A survey on deep learning in medical image analysis. Med. Image Anal. 42, 60\u201388 (2017)","journal-title":"Med. Image Anal."},{"key":"13_CR12","unstructured":"McMahan, B., Moore, E., Ramage, D., Hampson, S., y Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273\u20131282. PMLR (2017)"},{"key":"13_CR13","doi-asserted-by":"crossref","unstructured":"Milz, S., Arbeiter, G., Witt, C., Abdallah, B., Yogamani, S.: Visual slam for automated driving: exploring the applications of deep learning, pp. 247\u2013257 (2018)","DOI":"10.1109\/CVPRW.2018.00062"},{"issue":"5","key":"13_CR14","first-page":"851","volume":"18","author":"S Min","year":"2017","unstructured":"Min, S., Lee, B., Yoon, S.: Deep learning in bioinformatics. Brief. Bioinform. 18(5), 851\u2013869 (2017)","journal-title":"Brief. Bioinform."},{"key":"13_CR15","doi-asserted-by":"crossref","unstructured":"Rombach, R., Blattmann, A., Lorenz, D., Esser, P., Ommer, B.: High-resolution image synthesis with latent diffusion models. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10684\u201310695 (2022)","DOI":"10.1109\/CVPR52688.2022.01042"},{"key":"13_CR16","unstructured":"Sun, Z., Kairouz, P., Suresh, A.T., McMahan, H.B.: Can you really backdoor federated learning? arXiv preprint arXiv:1911.07963 (2019)"},{"key":"13_CR17","doi-asserted-by":"publisher","first-page":"911","DOI":"10.1109\/TIFS.2019.2929409","volume":"15","author":"G Xu","year":"2020","unstructured":"Xu, G., Li, H., Liu, S., Yang, K., Lin, X.: Verifynet: secure and verifiable federated learning. IEEE Trans. Inf. Forensics Secur. 15, 911\u2013926 (2020). https:\/\/doi.org\/10.1109\/TIFS.2019.2929409","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"issue":"3","key":"13_CR18","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1109\/MCI.2018.2840738","volume":"13","author":"T Young","year":"2018","unstructured":"Young, T., Hazarika, D., Poria, S., Cambria, E.: Recent trends in deep learning based natural language processing. IEEE Comput. Intell. Mag. 13(3), 55\u201375 (2018)","journal-title":"IEEE Comput. Intell. Mag."},{"key":"13_CR19","unstructured":"Zhao, B., Mopuri, K.R., Bilen, H.: idlg: improved deep leakage from gradients. arXiv preprint arXiv:2001.02610 (2020)"},{"key":"13_CR20","unstructured":"Zhu, L., Liu, Z., Han, S.: Deep leakage from gradients. In: Advances in Neural Information Processing Systems, vol. 32 (2019)"}],"container-title":["Communications in Computer and Information Science","Ubiquitous Security"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-97-1274-8_13","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T19:22:06Z","timestamp":1710271326000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-97-1274-8_13"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9789819712731","9789819712748"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-981-97-1274-8_13","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"13 March 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"UbiSec","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Ubiquitous Security","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Exeter","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","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":"1 November 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3 November 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ubisec2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/hpcn.exeter.ac.uk\/ubisec2023\/","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":"MyReview","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"91","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":"29","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":"0","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":"32% - 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":"4","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}