{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T11:31:28Z","timestamp":1743075088823,"version":"3.40.3"},"publisher-location":"Cham","reference-count":25,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030553036"},{"type":"electronic","value":"9783030553043"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-55304-3_23","type":"book-chapter","created":{"date-parts":[[2020,8,7]],"date-time":"2020-08-07T16:04:03Z","timestamp":1596816243000},"page":"447-460","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["PALOR: Poisoning Attacks Against Logistic Regression"],"prefix":"10.1007","author":[{"given":"Jialin","family":"Wen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Benjamin Zi Hao","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Minhui","family":"Xue","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haifeng","family":"Qian","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,8,6]]},"reference":[{"key":"23_CR1","unstructured":"Alfeld, S., Zhu, X., Barford, P.: Data poisoning attacks against autoregressive models. In: Schuurmans, D., Wellman, M.P. (eds.) Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, 12\u201317 February 2016, Phoenix, Arizona, USA, pp. 1452\u20131458. AAAI Press (2016). http:\/\/www.aaai.org\/ocs\/index.php\/AAAI\/AAAI16\/paper\/view\/12049"},{"key":"23_CR2","unstructured":"Bach, F.R., Blei, D.M. (eds.): Proceedings of the 32nd International Conference on Machine Learning, ICML 2015, Lille, France, 6\u201311 July 2015, JMLR Workshop and Conference Proceedings, vol. 37. JMLR.org (2015). http:\/\/proceedings.mlr.press\/v37\/"},{"key":"23_CR3","doi-asserted-by":"publisher","unstructured":"Barni, M., Kallas, K., Tondi, B.: A new backdoor attack in CNNS by training set corruption without label poisoning. In: 2019 IEEE International Conference on Image Processing, ICIP 2019, Taipei, Taiwan, 22\u201325 September 2019, pp. 101\u2013105. IEEE (2019). https:\/\/doi.org\/10.1109\/ICIP.2019.8802997","DOI":"10.1109\/ICIP.2019.8802997"},{"key":"23_CR4","unstructured":"Biggio, B., Nelson, B., Laskov, P.: Poisoning attacks against support vector machines. In: Proceedings of the 29th International Conference on Machine Learning, ICML 2012, Edinburgh, Scotland, UK, 26 June\u20131 July 2012 (2012). http:\/\/icml.cc\/2012\/papers\/880.pdf"},{"key":"23_CR5","unstructured":"Chen, X., Liu, C., Li, B., Lu, K., Song, D.: Targeted backdoor attacks on deep learning systems using data poisoning. CoRR abs\/1712.05526 (2017). http:\/\/arxiv.org\/abs\/1712.05526"},{"key":"23_CR6","unstructured":"Dua, D., Graff, C.: UCI machine learning repository (2017). http:\/\/archive.ics.uci.edu\/ml\/datasets\/Wine"},{"key":"23_CR7","unstructured":"Dua, D., Graff, C.: UCI machine learning repository (2017). http:\/\/archive.ics.uci.edu\/ml\/datasets\/Adult"},{"key":"23_CR8","unstructured":"Dua, D., Graff, C.: UCI machine learning repository (2017).http:\/\/archive.ics.uci.edu\/ml\/datasets\/Breast+Cancer+Wisconsin+%28Diagnostic%29"},{"key":"23_CR9","unstructured":"Fang, M., Cao, X., Jia, J., Gong, N.Z.: Local model poisoning attacks to byzantine-robust federated learning. CoRR abs\/1911.11815 (2019). http:\/\/arxiv.org\/abs\/1911.11815"},{"key":"23_CR10","doi-asserted-by":"publisher","unstructured":"Fang, M., Yang, G., Gong, N.Z., Liu, J.: Poisoning attacks to graph-based recommender systems. In: Proceedings of the 34th Annual Computer Security Applications Conference, ACSAC 2018, San Juan, PR, USA, 03\u201307 December 2018, pp. 381\u2013392. ACM (2018). https:\/\/doi.org\/10.1145\/3274694.3274706","DOI":"10.1145\/3274694.3274706"},{"key":"23_CR11","unstructured":"Gu, T., Dolan-Gavitt, B., Garg, S.: BadNets: identifying vulnerabilities in the machine learning model supply chain. CoRR abs\/1708.06733 (2017). http:\/\/arxiv.org\/abs\/1708.06733"},{"key":"23_CR12","doi-asserted-by":"publisher","unstructured":"Jagielski, M., Oprea, A., Biggio, B., Liu, C., Nita-Rotaru, C., Li, B.: Manipulating machine learning: Poisoning attacks and countermeasures for regression learning. In: Proceedings of 2018 IEEE Symposium on Security and Privacy, SP 2018, 21\u201323 May 2018, San Francisco, California, USA, pp. 19\u201335 (2018). https:\/\/doi.org\/10.1109\/SP.2018.00057","DOI":"10.1109\/SP.2018.00057"},{"key":"23_CR13","unstructured":"Li, B., Wang, Y., Singh, A., Vorobeychik, Y.: Data poisoning attacks on factorization-based collaborative filtering. In: Lee, D.D., Sugiyama, M., von Luxburg, U., Guyon, I., Garnett, R. (eds.) Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 5\u201310 December 2016, Barcelona, Spain, pp. 1885\u20131893 (2016)"},{"key":"23_CR14","unstructured":"Li, S., Zhao, B.Z.H., Xue, M., Kaafar, D., Zhu, H.: Invisible backdoor attacks against deep neural networks. CoRR abs\/1909.02742 (2019). http:\/\/arxiv.org\/abs\/1909.02742"},{"key":"23_CR15","unstructured":"Liao, C., Zhong, H., Squicciarini, A.C., Zhu, S., Miller, D.J.: Backdoor embedding in convolutional neural network models via invisible perturbation. CoRR abs\/1808.10307 (2018). http:\/\/arxiv.org\/abs\/1808.10307"},{"key":"23_CR16","doi-asserted-by":"publisher","unstructured":"Ma, Y., Zhu, X., Hsu, J.: Data poisoning against differentially-private learners: attacks and defenses. In: Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI 2019, Macao, China, 10\u201316 August 2019, pp. 4732\u20134738 (2019). https:\/\/doi.org\/10.24963\/ijcai.2019\/657","DOI":"10.24963\/ijcai.2019\/657"},{"key":"23_CR17","doi-asserted-by":"crossref","unstructured":"Mu\u00f1oz-Gonz\u00e1lez, L., et al.: Towards poisoning of deep learning algorithms with back-gradient optimization. In: Proceedings of the 10th ACM Workshop on Artificial Intelligence and Security, pp. 27\u201338 (2017)","DOI":"10.1145\/3128572.3140451"},{"key":"23_CR18","unstructured":"Nelson, B., et al.: Exploiting machine learning to subvert your spam filter. In: Monrose, F. (ed.) Proceedings of First USENIX Workshop on Large-Scale Exploits and Emergent Threats, LEET 2008, San Francisco, CA, USA, 15 April 2008. USENIX Association (2008). http:\/\/www.usenix.org\/events\/leet08\/tech\/full_papers\/nelson\/nelson.pdf"},{"key":"23_CR19","doi-asserted-by":"publisher","unstructured":"Rubinstein, B.I.P., et al.: ANTIDOTE: understanding and defending against poisoning of anomaly detectors. In: Feldmann, A., Mathy, L. (eds.) Proceedings of the 9th ACM SIGCOMM Internet Measurement Conference, IMC 2009, Chicago, Illinois, USA, 4\u20136 November 2009, pp. 1\u201314. ACM (2009). https:\/\/doi.org\/10.1145\/1644893.1644895","DOI":"10.1145\/1644893.1644895"},{"key":"23_CR20","unstructured":"Saha, A., Subramanya, A., Pirsiavash, H.: Hidden trigger backdoor attacks. CoRR abs\/1910.00033 (2019). http:\/\/arxiv.org\/abs\/1910.00033"},{"key":"23_CR21","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 2017, San Jose, CA, USA, 22\u201326 May 2017, pp. 3\u201318 (2017). https:\/\/doi.org\/10.1109\/SP.2017.41","DOI":"10.1109\/SP.2017.41"},{"key":"23_CR22","doi-asserted-by":"publisher","unstructured":"Wang, B., Gong, N.Z.: Attacking graph-based classification via manipulating the graph structure. In: Cavallaro, L., Kinder, J., Wang, X., Katz, J. (eds.) Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security, CCS 2019, London, UK, 11\u201315 November 2019, pp. 2023\u20132040. ACM (2019). https:\/\/doi.org\/10.1145\/3319535.3354206","DOI":"10.1145\/3319535.3354206"},{"key":"23_CR23","doi-asserted-by":"publisher","unstructured":"Xiao, H., Xiao, H., Eckert, C.: Adversarial label flips attack on support vector machines. In: Raedt, L.D., et al. (eds.) ECAI 2012\u201320th European Conference on Artificial Intelligence. Including Prestigious Applications of Artificial Intelligence (PAIS-2012) System Demonstrations Track, Montpellier, France, 27\u201331 August 2012. Frontiers in Artificial Intelligence and Applications, vol. 242, pp. 870\u2013875. IOS Press (2012). https:\/\/doi.org\/10.3233\/978-1-61499-098-7-870","DOI":"10.3233\/978-1-61499-098-7-870"},{"key":"23_CR24","doi-asserted-by":"crossref","unstructured":"Yang, G., Gong, N.Z., Cai, Y.: Fake co-visitation injection attacks to recommender systems. In: 24th Annual Network and Distributed System Security Symposium, NDSS 2017, San Diego, California, USA, 26 February\u20131 March 2017. The Internet Society (2017). https:\/\/www.ndss-symposium.org\/ndss2017\/ndss-2017-programme\/fake-co-visitation-injection-attacks-recommender-systems\/","DOI":"10.14722\/ndss.2017.23020"},{"key":"23_CR25","doi-asserted-by":"publisher","unstructured":"Z\u00fcgner, D., Akbarnejad, A., G\u00fcnnemann, S.: Adversarial attacks on neural networks for graph data. In: Kraus, S. (ed.) Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI 2019, Macao, China, 10\u201316 August 2019, pp. 6246\u20136250. ijcai.org (2019). https:\/\/doi.org\/10.24963\/ijcai.2019\/872","DOI":"10.24963\/ijcai.2019\/872"}],"container-title":["Lecture Notes in Computer Science","Information Security and Privacy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-55304-3_23","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,4,23]],"date-time":"2021-04-23T17:47:30Z","timestamp":1619200050000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-55304-3_23"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030553036","9783030553043"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-55304-3_23","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"6 August 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ACISP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Australasian Conference on Information Security and Privacy","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Perth, WA","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Australia","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 November 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2 December 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"acisp2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/nsclab.org\/acisp2020\/","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":"easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"151","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":"31","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":"5","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":"21% - 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":"3,7","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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"The conference was held virtually due to COVID-19 pandemic.","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)"}}]}}