{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T13:49:39Z","timestamp":1762004979996,"version":"3.41.0"},"publisher-location":"New York, New York, USA","reference-count":59,"publisher":"ACM Press","license":[{"start":{"date-parts":[[2018,1,1]],"date-time":"2018-01-01T00:00:00Z","timestamp":1514764800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"US National Science Foundation","award":["IIS-1553411","CNS-1742845","CNS-1566374","CNS-1652503"],"award-info":[{"award-number":["IIS-1553411","CNS-1742845","CNS-1566374","CNS-1652503"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018]]},"DOI":"10.1145\/3178876.3186032","type":"proceedings-article","created":{"date-parts":[[2018,4,13]],"date-time":"2018-04-13T15:53:48Z","timestamp":1523634828000},"page":"13-22","source":"Crossref","is-referenced-by-count":53,"title":["Attack under Disguise"],"prefix":"10.1145","author":[{"given":"Chenglin","family":"Miao","sequence":"first","affiliation":[{"name":"State University of New York at Buffalo, Buffalo, NY, USA"}]},{"given":"Qi","family":"Li","sequence":"additional","affiliation":[{"name":"University of Illinois at Urbana-Champaign, Urbana, IL, USA"}]},{"given":"Lu","family":"Su","sequence":"additional","affiliation":[{"name":"State University of New York at Buffalo, Buffalo, NY, USA"}]},{"given":"Mengdi","family":"Huai","sequence":"additional","affiliation":[{"name":"State University of New York at Buffalo, Buffalo, NY, USA"}]},{"given":"Wenjun","family":"Jiang","sequence":"additional","affiliation":[{"name":"State University of New York at Buffalo, Buffalo, NY, USA"}]},{"given":"Jing","family":"Gao","sequence":"additional","affiliation":[{"name":"State University of New York at Buffalo, Buffalo, NY, USA"}]}],"member":"320","reference":[{"key":"key-10.1145\/3178876.3186032-1","doi-asserted-by":"crossref","unstructured":"Scott Alfeld, Xiaojin Zhu, and Paul Barford . 2016. Data Poisoning Attacks against Autoregressive Models Proc. of AAAI. 1452--1458.","DOI":"10.1609\/aaai.v30i1.10237"},{"key":"key-10.1145\/3178876.3186032-2","unstructured":"Jonathan F Bard . 1998. Practical bilevel optimization: algorithms and applications. Kluwer Academic Publishers."},{"key":"key-10.1145\/3178876.3186032-3","doi-asserted-by":"crossref","unstructured":"Marco Barreno, Blaine Nelson, Russell Sears, Anthony D Joseph, and J Doug Tygar . 2006. Can machine learning be secure? .In Proc. of ASIACCS. 16--25.","DOI":"10.1145\/1128817.1128824"},{"key":"key-10.1145\/3178876.3186032-4","unstructured":"Battista Biggio, Blaine Nelson, and Pavel Laskov . 2012. Poisoning attacks against support vector machines. In Proc. of ICML."},{"key":"key-10.1145\/3178876.3186032-5","doi-asserted-by":"crossref","unstructured":"Marco Brambilla, Stefano Ceri, Andrea Mauri, and Riccardo Volonterio . 2014. Community-based crowdsourcing. In Proc. of WWW. 891--896.","DOI":"10.1145\/2567948.2578835"},{"key":"key-10.1145\/3178876.3186032-6","doi-asserted-by":"crossref","unstructured":"Shih-Hao Chang and Zhi-Rong Chen . 2016. Protecting Mobile Crowd Sensing against Sybil Attacks Using Cloud Based Trust Management System. Mobile Information Systems Vol. 2016 (2016).","DOI":"10.1155\/2016\/6506341"},{"key":"key-10.1145\/3178876.3186032-7","unstructured":"Xi Chen, Qihang Lin, and Dengyong Zhou . 2013. Optimistic knowledge gradient policy for optimal budget allocation in crowdsourcing Proc. of ICML. 64--72."},{"key":"key-10.1145\/3178876.3186032-8","doi-asserted-by":"crossref","unstructured":"Nilesh Dalvi, Anirban Dasgupta, Ravi Kumar, and Vibhor Rastogi . 2013. Aggregating crowdsourced binary ratings. In Proc. of WWW. 285--294.","DOI":"10.1145\/2488388.2488414"},{"key":"key-10.1145\/3178876.3186032-9","doi-asserted-by":"crossref","unstructured":"Alexander Philip Dawid and Allan M Skene . 1979. Maximum likelihood estimation of observer error-rates using the EM algorithm. Applied statistics (1979).","DOI":"10.2307\/2346806"},{"key":"key-10.1145\/3178876.3186032-10","doi-asserted-by":"crossref","unstructured":"Luca de Alfaro, Vassilis Polychronopoulos, and Michael Shavlovsky . 2015. Reliable aggregation of boolean crowdsourced tasks Proc. of HCOMP.","DOI":"10.1609\/hcomp.v3i1.13240"},{"key":"key-10.1145\/3178876.3186032-11","unstructured":"Arthur P Dempster, Nan M Laird, and Donald B Rubin . 1977. Maximum likelihood from incomplete data via the EM algorithm. Journal of the royal statistical society (1977), 1--38."},{"key":"key-10.1145\/3178876.3186032-12","unstructured":"Djellel Eddine Difallah, Gianluca Demartini, and Philippe Cudré-Mauroux . 2012. Mechanical Cheat: Spamming Schemes and Adversarial Techniques on Crowdsourcing Platforms.. In CrowdSearch. 26--30."},{"key":"key-10.1145\/3178876.3186032-13","doi-asserted-by":"crossref","unstructured":"Djellel Eddine Difallah, Gianluca Demartini, and Philippe Cudré-Mauroux . 2016. Scheduling human intelligence tasks in multi-tenant crowd-powered systems Proc. of WWW. 855--865.","DOI":"10.1145\/2872427.2883030"},{"key":"key-10.1145\/3178876.3186032-14","unstructured":"Carsten Eickhoff and Arjen P de Vries . 2013. Increasing cheat robustness of crowdsourcing tasks. Information retrieval Vol. 16, 2 (2013), 121--137."},{"key":"key-10.1145\/3178876.3186032-15","doi-asserted-by":"crossref","unstructured":"Ju Fan, Guoliang Li, Beng Chin Ooi, Kian-lee Tan, and Jianhua Feng . 2015. icrowd: An adaptive crowdsourcing framework. In Proc. of SIGMOD. 1015--1030.","DOI":"10.1145\/2723372.2750550"},{"key":"key-10.1145\/3178876.3186032-16","doi-asserted-by":"crossref","unstructured":"Ujwal Gadiraju, Gianluca Demartini, Ricardo Kawase, and Stefan Dietze . 2015 a. Human beyond the machine: Challenges and opportunities of microtask crowdsourcing. IEEE Intelligent Systems Vol. 30, 4 (2015), 81--85.","DOI":"10.1109\/MIS.2015.66"},{"key":"key-10.1145\/3178876.3186032-17","doi-asserted-by":"crossref","unstructured":"Ujwal Gadiraju, Ricardo Kawase, Stefan Dietze, and Gianluca Demartini . 2015 b. Understanding malicious behavior in crowdsourcing platforms: The case of online surveys Proc. of CHI. 1631--1640.","DOI":"10.1145\/2702123.2702443"},{"key":"key-10.1145\/3178876.3186032-18","unstructured":"Matthias Hirth, Tobias Hoßfeld, and Phuoc Tran-Gia . 2010. Cheat-detection mechanisms for crowdsourcing. University of Würzburg, Tech. Rep Vol. 4 (2010)."},{"key":"key-10.1145\/3178876.3186032-19","doi-asserted-by":"crossref","unstructured":"Ling Huang, Anthony D Joseph, Blaine Nelson, Benjamin IP Rubinstein, and JD Tygar . 2011. Adversarial machine learning. In Proc. of AISec. 43--58.","DOI":"10.1145\/2046684.2046692"},{"key":"key-10.1145\/3178876.3186032-20","doi-asserted-by":"crossref","unstructured":"Nguyen Quoc Viet Hung, Duong Chi Thang, Matthias Weidlich, and Karl Aberer . 2015. Minimizing efforts in validating crowd answers. In Proc. of SIGMOD. 999--1014.","DOI":"10.1145\/2723372.2723731"},{"key":"key-10.1145\/3178876.3186032-21","doi-asserted-by":"crossref","unstructured":"Vittorio P Illiano and Emil C Lupu . 2015. Detecting malicious data injections in wireless sensor networks: A survey. ACM Computing Surveys (CSUR) (2015).","DOI":"10.1145\/2818184"},{"key":"key-10.1145\/3178876.3186032-22","doi-asserted-by":"crossref","unstructured":"Panagiotis G Ipeirotis, Foster Provost, and Jing Wang . 2010. Quality management on amazon mechanical turk. In Proc. of the ACM SIGKDD workshop on human computation. 64--67.","DOI":"10.1145\/1837885.1837906"},{"key":"key-10.1145\/3178876.3186032-23","unstructured":"Srikanth Jagabathula, Lakshminarayanan Subramanian, and Ashwin Venkataraman . 2014. Reputation-based worker filtering in crowdsourcing Proc. of NIPS. 2492--2500."},{"key":"key-10.1145\/3178876.3186032-24","unstructured":"Srikanth Jagabathula, Lakshminarayanan Subramanian, and Ashwin Venkataraman . 2016. Identifying Unreliable and Adversarial Workers in Crowdsourced Labeling Tasks. (2016)."},{"key":"key-10.1145\/3178876.3186032-25","unstructured":"David R Karger, Sewoong Oh, and Devavrat Shah . 2014. Budget-optimal task allocation for reliable crowdsourcing systems. Operations Research Vol. 62, 1 (2014), 1--24."},{"key":"key-10.1145\/3178876.3186032-26","doi-asserted-by":"crossref","unstructured":"Walter S Lasecki, Jaime Teevan, and Ece Kamar . 2014. Information extraction and manipulation threats in crowd-powered systems Proc. of CSCW. 248--256.","DOI":"10.1145\/2531602.2531733"},{"key":"key-10.1145\/3178876.3186032-27","unstructured":"Edith Law, Ming Yin, Joslin Goh, Kevin Chen, Michael A Terry, and Krzysztof Z Gajos . 2016. Curiosity killed the cat, but makes crowdwork better Proc. of CHI. 4098--4110."},{"key":"key-10.1145\/3178876.3186032-28","unstructured":"Bo Li, Yining Wang, Aarti Singh, and Yevgeniy Vorobeychik . 2016 b. Data poisoning attacks on factorization-based collaborative filtering Proc. of NIPS. 1885--1893."},{"key":"key-10.1145\/3178876.3186032-29","doi-asserted-by":"crossref","unstructured":"Guoliang Li, Jiannan Wang, Yudian Zheng, and Michael J Franklin . 2016 c. Crowdsourced data management: A survey. IEEE Transactions on Knowledge and Data Engineering Vol. 28, 9 (2016), 2296--2319.","DOI":"10.1109\/TKDE.2016.2535242"},{"key":"key-10.1145\/3178876.3186032-30","unstructured":"Hongwei Li, Bin Yu, and Dengyong Zhou . 2013. Error rate analysis of labeling by crowdsourcing. In ICML Workshop: Machine Learning Meets Crowdsourcing."},{"key":"key-10.1145\/3178876.3186032-31","doi-asserted-by":"crossref","unstructured":"Qi Li, Fenglong Ma, Jing Gao, Lu Su, and Christopher J Quinn . 2016 a. Crowdsourcing high quality labels with a tight budget Proc. of WSDM. 237--246.","DOI":"10.1145\/2835776.2835797"},{"key":"key-10.1145\/3178876.3186032-32","doi-asserted-by":"crossref","unstructured":"Yaliang Li, Jing Gao, Patrick PC Lee, Lu Su, Caifeng He, Cheng He, Fan Yang, and Wei Fan . 2017. A weighted crowdsourcing approach for network quality measurement in cellular data networks. IEEE Transactions on Mobile Computing Vol. 16, 2 (2017), 300--313.","DOI":"10.1109\/TMC.2016.2546900"},{"key":"key-10.1145\/3178876.3186032-33","unstructured":"Bing Liu . 2012. Sentiment analysis and opinion mining. Synthesis lectures on human language technologies Vol. 5, 1 (2012), 1--167."},{"key":"key-10.1145\/3178876.3186032-34","unstructured":"Qiang Liu, Jian Peng, and Alexander T Ihler . 2012. Variational inference for crowdsourcing. In Proc. of NIPS. 692--700."},{"key":"key-10.1145\/3178876.3186032-35","doi-asserted-by":"crossref","unstructured":"Yao Liu, Peng Ning, and Michael K Reiter . 2011. False data injection attacks against state estimation in electric power grids. ACM Transactions on Information and System Security Vol. 14, 1 (2011), 13.","DOI":"10.1145\/1952982.1952995"},{"key":"key-10.1145\/3178876.3186032-36","doi-asserted-by":"crossref","unstructured":"Fenglong Ma, Yaliang Li, Qi Li, Minghui Qiu, Jing Gao, Shi Zhi, Lu Su, Bo Zhao, Heng Ji, and Jiawei Han . 2015. Faitcrowd: Fine grained truth discovery for crowdsourced data aggregation Proc. of KDD. 745--754.","DOI":"10.1145\/2783258.2783314"},{"key":"key-10.1145\/3178876.3186032-37","doi-asserted-by":"crossref","unstructured":"Shike Mei and Xiaojin Zhu . 2015. Using Machine Teaching to Identify Optimal Training-Set Attacks on Machine Learners. In Proc. of AAAI. 2871--2877.","DOI":"10.1609\/aaai.v29i1.9569"},{"key":"key-10.1145\/3178876.3186032-38","doi-asserted-by":"crossref","unstructured":"Chuishi Meng, Wenjun Jiang, Yaliang Li, Jing Gao, Lu Su, Hu Ding, and Yun Cheng . 2015. Truth discovery on crowd sensing of correlated entities Proc. of SenSys. 169--182.","DOI":"10.1145\/2809695.2809715"},{"key":"key-10.1145\/3178876.3186032-39","doi-asserted-by":"crossref","unstructured":"Chenglin Miao, Wenjun Jiang, Lu Su, Yaliang Li, Suxin Guo, Zhan Qin, Houping Xiao, Jing Gao, and Kui Ren . 2015. Cloud-enabled privacy-preserving truth discovery in crowd sensing systems Proc. of SenSys. 183--196.","DOI":"10.1145\/2809695.2809719"},{"key":"key-10.1145\/3178876.3186032-40","doi-asserted-by":"crossref","unstructured":"Quoc Viet Hung Nguyen, Tam Nguyen Thanh, Ngoc Tran Lam, Son Thanh Do, and Karl Aberer . 2013. A Benchmark for Aggregation Techniques in Crowdsourcing Proc. of SIGIR.","DOI":"10.1007\/978-3-642-41154-0_1"},{"key":"key-10.1145\/3178876.3186032-41","unstructured":"Jungseul Ok, Sewoong Oh, Jinwoo Shin, and Yung Yi . 2016. Optimality of belief propagation for crowdsourced classification Proc. of ICML. 535--544."},{"key":"key-10.1145\/3178876.3186032-42","doi-asserted-by":"crossref","unstructured":"Zhengrui Qin, Qun Li, and George Hsieh . 2013. Defending against cooperative attacks in cooperative spectrum sensing. IEEE Transactions on Wireless Communications Vol. 12, 6 (2013), 2680--2687.","DOI":"10.1109\/TWC.2013.041913.120516"},{"key":"key-10.1145\/3178876.3186032-43","unstructured":"Vikas C Raykar and Shipeng Yu . 2012. Eliminating spammers and ranking annotators for crowdsourced labeling tasks. Journal of Machine Learning Research Vol. 13, Feb (2012), 491--518."},{"key":"key-10.1145\/3178876.3186032-44","unstructured":"Vikas C Raykar, Shipeng Yu, Linda H Zhao, Gerardo Hermosillo Valadez, Charles Florin, Luca Bogoni, and Linda Moy . 2010. Learning from crowds. Journal of Machine Learning Research Vol. 11, Apr (2010), 1297--1322."},{"key":"key-10.1145\/3178876.3186032-45","doi-asserted-by":"crossref","unstructured":"Mohsen Rezvani, Aleksandar Ignjatovic, Elisa Bertino, and Sanjay Jha . 2015. Secure data aggregation technique for wireless sensor networks in the presence of collusion attacks. IEEE Transactions on Dependable and Secure Computing Vol. 12, 1 (2015), 98--110.","DOI":"10.1109\/TDSC.2014.2316816"},{"key":"key-10.1145\/3178876.3186032-46","doi-asserted-by":"crossref","unstructured":"Rion Snow, Brendan O'Connor, Daniel Jurafsky, and Andrew Y Ng . 2008. Cheap and fast--but is it good?: evaluating non-expert annotations for natural language tasks. In Proc. of the EMNLP. 254--263.","DOI":"10.3115\/1613715.1613751"},{"key":"key-10.1145\/3178876.3186032-47","doi-asserted-by":"crossref","unstructured":"Norases Vesdapunt, Kedar Bellare, and Nilesh Dalvi . 2014. Crowdsourcing algorithms for entity resolution. Proceedings of the VLDB Endowment Vol. 7, 12 (2014), 1071--1082.","DOI":"10.14778\/2732977.2732982"},{"key":"key-10.1145\/3178876.3186032-48","unstructured":"Jeroen Vuurens, Arjen P de Vries, and Carsten Eickhoff . 2011. How much spam can you take? an analysis of crowdsourcing results to increase accuracy. In Proc. of CIR. 21--26."},{"key":"key-10.1145\/3178876.3186032-49","doi-asserted-by":"crossref","unstructured":"Gang Wang, Bolun Wang, Tianyi Wang, Ana Nika, Haitao Zheng, and Ben Y Zhao . 2016. Defending against sybil devices in crowdsourced mapping services Proc. of MobiSys. 179--191.","DOI":"10.1145\/2906388.2906420"},{"key":"key-10.1145\/3178876.3186032-50","unstructured":"Gang Wang, Tianyi Wang, Haitao Zheng, and Ben Y Zhao . 2014. Man vs. Machine: Practical Adversarial Detection of Malicious Crowdsourcing Workers. USENIX Security Symposium. 239--254."},{"key":"key-10.1145\/3178876.3186032-51","unstructured":"Jiannan Wang, Tim Kraska, Michael J Franklin, and Jianhua Feng . 2012. Crowder: Crowdsourcing entity resolution. Proc. of the VLDB Endowment Vol. 5, 11 (2012), 1483--1494."},{"key":"key-10.1145\/3178876.3186032-52","doi-asserted-by":"crossref","unstructured":"Peter Welinder and Pietro Perona . 2010. Online crowdsourcing: rating annotators and obtaining cost-effective labels Proc. of CVPRW. 25--32.","DOI":"10.1109\/CVPRW.2010.5543189"},{"key":"key-10.1145\/3178876.3186032-53","unstructured":"Jacob Whitehill, Ting-fan Wu, Jacob Bergsma, Javier R Movellan, and Paul L Ruvolo . 2009. Whose vote should count more: Optimal integration of labels from labelers of unknown expertise. In Proc. of NIPS. 2035--2043."},{"key":"key-10.1145\/3178876.3186032-54","unstructured":"Huang Xiao, Battista Biggio, Gavin Brown, Giorgio Fumera, Claudia Eckert, and Fabio Roli . 2015. Is feature selection secure against training data poisoning? Proc. of ICML. 1689--1698."},{"key":"key-10.1145\/3178876.3186032-55","doi-asserted-by":"crossref","unstructured":"Dong Yuan, Guoliang Li, Qi Li, and Yudian Zheng . 2017. Sybil Defense in Crowdsourcing Platforms. In Proc. of CIKM. 1529--1538.","DOI":"10.1145\/3132847.3133039"},{"key":"key-10.1145\/3178876.3186032-56","doi-asserted-by":"crossref","unstructured":"Kuan Zhang, Xiaohui Liang, Rongxing Lu, and Xuemin Shen . 2014 b. Sybil attacks and their defenses in the internet of things. IEEE Internet of Things Journal Vol. 1, 5 (2014), 372--383.","DOI":"10.1109\/JIOT.2014.2344013"},{"key":"key-10.1145\/3178876.3186032-57","unstructured":"Yuchen Zhang, Xi Chen, Denny Zhou, and Michael I Jordan . 2014 a. Spectral methods meet EM: A provably optimal algorithm for crowdsourcing Proc. of NIPS. 1260--1268."},{"key":"key-10.1145\/3178876.3186032-58","unstructured":"Yudian Zheng, Guoliang Li, Yuanbing Li, Caihua Shan, and Reynold Cheng . 2017. Truth inference in crowdsourcing: is the problem solved? Proc. of the VLDB Endowment Vol. 10, 5 (2017), 541--552."},{"key":"key-10.1145\/3178876.3186032-59","unstructured":"Denny Zhou, Sumit Basu, Yi Mao, and John C Platt . 2012. Learning from the wisdom of crowds by minimax entropy Proc. of NIPS. 2195--2203."}],"event":{"number":"2018","sponsor":["SIGWEB, ACM Special Interest Group on Hypertext, Hypermedia, and Web","IW3C2, International World Wide Web Conference Committee"],"acronym":"WWW '18","name":"the 2018 World Wide Web Conference","start":{"date-parts":[[2018,4,23]]},"location":"Lyon, France","end":{"date-parts":[[2018,4,27]]}},"container-title":["Proceedings of the 2018 World Wide Web Conference on World Wide Web - WWW '18"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3178876.3186032","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/dl.acm.org\/ft_gateway.cfm?id=3186032&ftid=1957521&dwn=1","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,3]],"date-time":"2025-07-03T17:24:47Z","timestamp":1751563487000},"score":1,"resource":{"primary":{"URL":"http:\/\/dl.acm.org\/citation.cfm?doid=3178876.3186032"}},"subtitle":["An Intelligent Data Poisoning Attack Mechanism in Crowdsourcing"],"proceedings-subject":"World Wide Web","short-title":[],"issued":{"date-parts":[[2018]]},"references-count":59,"URL":"https:\/\/doi.org\/10.1145\/3178876.3186032","relation":{},"subject":[],"published":{"date-parts":[[2018]]}}}