{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,6]],"date-time":"2026-01-06T13:34:24Z","timestamp":1767706464385,"version":"3.40.3"},"publisher-location":"Cham","reference-count":20,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031306365"},{"type":"electronic","value":"9783031306372"}],"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\/978-3-031-30637-2_42","type":"book-chapter","created":{"date-parts":[[2023,4,13]],"date-time":"2023-04-13T10:08:13Z","timestamp":1681380493000},"page":"627-643","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Towards Defending Against Byzantine LDP Amplified Gain Attacks"],"prefix":"10.1007","author":[{"given":"Yukun","family":"Yan","sequence":"first","affiliation":[]},{"given":"Qingqing","family":"Ye","sequence":"additional","affiliation":[]},{"given":"Haibo","family":"Hu","sequence":"additional","affiliation":[]},{"given":"Rui","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Qilong","family":"Han","sequence":"additional","affiliation":[]},{"given":"Leixia","family":"Wang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,4,14]]},"reference":[{"key":"42_CR1","unstructured":"Acharya, J., Sun, Z., Zhang, H.: Hadamard response: estimating distributions privately, efficiently, and with little communication. In: Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (2019)"},{"key":"42_CR2","unstructured":"Bassily, R., Nissim, K., Stemmer, U., Guha Thakurta, A.: Practical locally private heavy hitters. In: Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"key":"42_CR3","doi-asserted-by":"crossref","unstructured":"Bassily, R., Smith, A.: Local, private, efficient protocols for succinct histograms. In: Proceedings of the 47th Annual ACM Symposium on Theory of Computing (2015)","DOI":"10.1145\/2746539.2746632"},{"key":"42_CR4","unstructured":"Cao, X., Jia, J., Gong, N.Z.: Data poisoning attacks to local differential privacy protocols. In: Proceedings of the 30th USENIX Security Symposium (2021)"},{"key":"42_CR5","doi-asserted-by":"crossref","unstructured":"Cheu, A., Smith, A., Ullman, J.: Manipulation attacks in local differential privacy. In: Proceedings of the 42nd IEEE Symposium on Security and Privacy (2021)","DOI":"10.1109\/SP40001.2021.00001"},{"issue":"11","key":"42_CR6","doi-asserted-by":"publisher","first-page":"2046","DOI":"10.14778\/3476249.3476261","volume":"14","author":"G Cormode","year":"2021","unstructured":"Cormode, G., Maddock, S., Maple, C.: Frequency estimation under local differential privacy. Proc. VLDB Endow. 14(11), 2046\u20132058 (2021)","journal-title":"Proc. VLDB Endow."},{"key":"42_CR7","doi-asserted-by":"crossref","unstructured":"Erlingsson, \u00da., Pihur, V., Korolova, A.: RAPPOR: randomized aggregatable privacy-preserving ordinal response. In: Proceedings of the 21st ACM Conference on Computer and Communications Security (2014)","DOI":"10.1145\/2660267.2660348"},{"issue":"4","key":"42_CR8","doi-asserted-by":"publisher","first-page":"767","DOI":"10.1007\/s10462-012-9364-9","volume":"42","author":"I Gunes","year":"2014","unstructured":"Gunes, I., Kaleli, C., Bilge, A., Polat, H.: Shilling attacks against recommender systems: a comprehensive survey. Artif. Intell. Rev. 42(4), 767\u2013799 (2014)","journal-title":"Artif. Intell. Rev."},{"key":"42_CR9","unstructured":"Kairouz, P., Oh, S., Viswanath, P.: Extremal mechanisms for local differential privacy. In: Advances in Neural Information Processing Systems, vol. 27 (2014)"},{"key":"42_CR10","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1007\/978-3-030-81242-3_3","volume-title":"Data and Applications Security and Privacy XXXV","author":"F Kato","year":"2021","unstructured":"Kato, F., Cao, Y., Yoshikawa, M.: Preventing manipulation attack in local differential privacy using verifiable randomization mechanism. In: Barker, K., Ghazinour, K. (eds.) DBSec 2021. LNCS, vol. 12840, pp. 43\u201360. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-81242-3_3"},{"key":"42_CR11","unstructured":"Li, X., Gong, N.Z., Li, N., Sun, W., Li, H.: Fine-grained poisoning attacks to local differential privacy protocols for mean and variance estimation. arXiv preprint arXiv:2205.11782 (2022)"},{"issue":"6","key":"42_CR12","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1109\/79.543975","volume":"13","author":"TK Moon","year":"1996","unstructured":"Moon, T.K.: The expectation-maximization algorithm. IEEE Signal Process. Mag. 13(6), 47\u201360 (1996)","journal-title":"IEEE Signal Process. Mag."},{"key":"42_CR13","unstructured":"Prakash, S., Avestimehr, A.S.: Mitigating byzantine attacks in federated learning. arXiv preprint arXiv:2010.07541 (2020)"},{"issue":"1","key":"42_CR14","first-page":"1","volume":"1","author":"W Tang","year":"2022","unstructured":"Tang, W., Tang, F.: The Poisson binomial distribution - old & new. Stat. Sci. 1(1), 1\u201312 (2022)","journal-title":"Stat. Sci."},{"key":"42_CR15","unstructured":"ADP Team: Learning with privacy at scale. Apple Mach. J1(8), 1\u201325 (2017)"},{"key":"42_CR16","unstructured":"Wang, T., Blocki, J., Li, N., Jha, S.: Locally differentially private protocols for frequency estimation. In: Proceedings of the 26th USENIX Security Symposium (2017)"},{"issue":"2","key":"42_CR17","doi-asserted-by":"publisher","first-page":"982","DOI":"10.1109\/TDSC.2019.2927695","volume":"18","author":"T Wang","year":"2019","unstructured":"Wang, T., Li, N., Jha, S.: Locally differentially private heavy hitter identification. IEEE Trans. Dependable Secure Comput. 18(2), 982\u2013993 (2019)","journal-title":"IEEE Trans. Dependable Secure Comput."},{"key":"42_CR18","unstructured":"Wu, Y., Cao, X., Jia, J., Gong, N.Z.: Poisoning attacks to local differential privacy protocols for key-value data. In: Proceedings of the 31st USENIX Security Symposium (2022)"},{"key":"42_CR19","doi-asserted-by":"crossref","unstructured":"Yang, J., Cheng, X., Su, S., Chen, R., Ren, Q., Liu, Y.: Collecting preference rankings under local differential privacy. In: Proceedings of the 35th IEEE International Conference on Data Engineering (2019)","DOI":"10.1109\/ICDE.2019.00151"},{"key":"42_CR20","doi-asserted-by":"crossref","unstructured":"Ye, Q., Hu, H., Meng, X., Zheng, H.: PrivKV: key-value data collection with local differential privacy. In: Proceedings of the 40th IEEE Symposium on Security and Privacy (2019)","DOI":"10.1109\/SP.2019.00018"}],"container-title":["Lecture Notes in Computer Science","Database Systems for Advanced Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-30637-2_42","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T17:16:57Z","timestamp":1710263817000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-30637-2_42"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031306365","9783031306372"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-30637-2_42","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"14 April 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DASFAA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Database Systems for Advanced Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Tianjin","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":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 April 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 April 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"dasfaa2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.tjudb.cn\/dasfaa2023\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Microsoft CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"652","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":"125","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":"66","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":"19% - 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":"7.3","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)"}}]}}