{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:28:07Z","timestamp":1750220887447,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":36,"publisher":"ACM","license":[{"start":{"date-parts":[[2019,8,19]],"date-time":"2019-08-19T00:00:00Z","timestamp":1566172800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100002790","name":"Natural Sciences and Engineering Research Council of Canada","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100002790","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2019,8,19]]},"DOI":"10.1145\/3343737.3343744","type":"proceedings-article","created":{"date-parts":[[2019,8,13]],"date-time":"2019-08-13T14:53:19Z","timestamp":1565707999000},"page":"69-75","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Brokered Agreements in Multi-Party Machine Learning"],"prefix":"10.1145","author":[{"given":"Clement","family":"Fung","sequence":"first","affiliation":[{"name":"University of British Columbia"}]},{"given":"Ivan","family":"Beschastnikh","sequence":"additional","affiliation":[{"name":"University of British Columbia"}]}],"member":"320","published-online":{"date-parts":[[2019,8,19]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"April 24 2019. Facebook sets aside $3bn for privacy probe. https:\/\/www.bbc.com\/news\/business-48045138  April 24 2019. Facebook sets aside $3bn for privacy probe. https:\/\/www.bbc.com\/news\/business-48045138"},{"key":"e_1_3_2_1_2_1","unstructured":"January 21 2019. Google hit with &pound;44m GDPR fine over ads. https:\/\/www.bbc.com\/news\/technology-46944696  January 21 2019. Google hit with &pound;44m GDPR fine over ads. https:\/\/www.bbc.com\/news\/technology-46944696"},{"key":"e_1_3_2_1_4_1","unstructured":"E. Bagdasaryan A. Veit Y. Hua D. Estrin and V. Shmatikov. 2018. How To Backdoor Federated Learning. ArXiv e-prints (2018). arXiv:cs.CR\/1807.00459  E. Bagdasaryan A. Veit Y. Hua D. Estrin and V. Shmatikov. 2018. How To Backdoor Federated Learning. ArXiv e-prints (2018). arXiv:cs.CR\/1807.00459"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-010-5188-5"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.5555\/3042573.3042761"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/3133956.3133982"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"crossref","unstructured":"L\u00e9on Bottou. 2010. Large-Scale Machine Learning with Stochastic Gradient Descent.  L\u00e9on Bottou. 2010. Large-Scale Machine Learning with Stochastic Gradient Descent.","DOI":"10.1007\/978-3-7908-2604-3_16"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.5555\/1251375.1251396"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1561\/0400000042"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/2810103.2813677"},{"key":"e_1_3_2_1_12_1","unstructured":"Matthew Fredrikson Eric Lantz Somesh Jha Simon Lin David Page and Thomas Ristenpart. 2014. Privacy in Pharmacogenetics: An End-to-End Case Study of Personalized Warfarin Dosing. In USENIX SEC.   Matthew Fredrikson Eric Lantz Somesh Jha Simon Lin David Page and Thomas Ristenpart. 2014. Privacy in Pharmacogenetics: An End-to-End Case Study of Personalized Warfarin Dosing. In USENIX SEC."},{"key":"e_1_3_2_1_13_1","unstructured":"Clement Fung Jamie Koerner Stewart Grant and Ivan Beschastnikh. 2018. Dancing in the Dark: Private Multi-Party Machine Learning in an Untrusted Setting. arXiv e-prints (2018). arXiv:cs.CR\/1811.09712  Clement Fung Jamie Koerner Stewart Grant and Ivan Beschastnikh. 2018. Dancing in the Dark: Private Multi-Party Machine Learning in an Untrusted Setting. arXiv e-prints (2018). arXiv:cs.CR\/1811.09712"},{"key":"e_1_3_2_1_14_1","unstructured":"Clement Fung Chris J. M. Yoon and Ivan Beschastnikh. 2018. Mitigating Sybils in Federated Learning Poisoning. ArXiv e-prints (2018). arXiv:cs.LG\/1808.04866  Clement Fung Chris J. M. Yoon and Ivan Beschastnikh. 2018. Mitigating Sybils in Federated Learning Poisoning. ArXiv e-prints (2018). arXiv:cs.LG\/1808.04866"},{"volume-title":"Differentially Private Federated Learning: A Client Level Perspective. NIPS Workshop: Machine Learning on the Phone and other Consumer Devices","year":"2017","author":"Geyer Robin C.","key":"e_1_3_2_1_15_1"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/3132747.3132757"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/3133956.3134012"},{"volume-title":"Gaia: Geo-Distributed Machine Learning Approaching LAN Speeds. In NSDI.","year":"2017","author":"Hsieh Kevin","key":"e_1_3_2_1_18_1"},{"volume-title":"Proceedings of the 4th ACM Workshop on Security and Artificial Intelligence (AISec).","author":"Huang Ling","key":"e_1_3_2_1_19_1"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.14778\/3229863.3236266"},{"volume-title":"Proceedings of the 22th International Conference on Artificial Intelligence and Statistics (AISTATS).","author":"Jia Ruoxi","key":"e_1_3_2_1_21_1"},{"volume-title":"Scaling Distributed Machine Learning with the Parameter Server. In 11th USENIX Symposium on Operating Systems Design and Implementation (OSDI 14)","year":"2014","author":"Li Mu","key":"e_1_3_2_1_22_1"},{"key":"e_1_3_2_1_23_1","unstructured":"M. Lichman. 2013. UCI Machine Learning Repository. http:\/\/archive.ics.uci.edu\/ml  M. Lichman. 2013. UCI Machine Learning Repository. http:\/\/archive.ics.uci.edu\/ml"},{"volume-title":"Proceedings of the 20th International Conference on Artificial Intelligence and Statistics.","year":"2017","author":"McMahan H. Brendan","key":"e_1_3_2_1_24_1"},{"key":"e_1_3_2_1_25_1","unstructured":"L. Melis C. Song E. De Cristofaro and V. Shmatikov. 2018. Inference Attacks Against Collaborative Learning. ArXiv e-prints (2018). arXiv:cs.CR\/1805.04049  L. Melis C. Song E. De Cristofaro and V. Shmatikov. 2018. Inference Attacks Against Collaborative Learning. ArXiv e-prints (2018). arXiv:cs.CR\/1805.04049"},{"volume-title":"Bitcoin: A peer-to-peer electronic cash system.","year":"2009","author":"Nakamoto Satoshi","key":"e_1_3_2_1_26_1"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.5555\/1387709.1387716"},{"key":"e_1_3_2_1_28_1","unstructured":"Olga Ohrimenko Felix Schuster Cedric Fournet Aastha Mehta Sebastian Nowozin Kapil Vaswani and Manuel Costa. 2016. Oblivious Multi-Party Machine Learning on Trusted Processors. In USENIX SEC.   Olga Ohrimenko Felix Schuster Cedric Fournet Aastha Mehta Sebastian Nowozin Kapil Vaswani and Manuel Costa. 2016. Oblivious Multi-Party Machine Learning on Trusted Processors. In USENIX SEC."},{"volume-title":"NeurIPS Workshop: Critiquing and Correcting Trends in Machine Learning","year":"2018","author":"Overdorf Rebekah","key":"e_1_3_2_1_29_1"},{"volume-title":"Hogwild: A Lock-Free Approach to Parallelizing Stochastic Gradient Descent. In Advances in Neural Information Processing Systems 24.","year":"2011","author":"Recht Benjamin","key":"e_1_3_2_1_30_1"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/1644893.1644895"},{"volume-title":"USENIX Workshop on Hot Topics in Cloud Computing (HotCloud 19)","year":"2019","author":"Shastri Supreeth","key":"e_1_3_2_1_32_1"},{"volume-title":"Biscotti: A Ledger for Private and Secure Peer-to-Peer Machine Learning. ArXiv e-prints","year":"2018","author":"Shayan Muhammad","key":"e_1_3_2_1_33_1"},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1145\/2810103.2813687"},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"crossref","unstructured":"Nick Szabo. 1997. Formalizing and Securing Relationships on Public Networks. First Monday 2 9 (1997).  Nick Szabo. 1997. Formalizing and Securing Relationships on Public Networks. First Monday 2 9 (1997).","DOI":"10.5210\/fm.v2i9.548"},{"key":"e_1_3_2_1_36_1","unstructured":"Jun Tang Aleksandra Korolova Xiaolong Bai Xueqiang Wang and XiaoFeng Wang. 2017. Privacy Loss in Apple's Implementation of Differential Privacy on MacOS 10.12. (2017).  Jun Tang Aleksandra Korolova Xiaolong Bai Xueqiang Wang and XiaoFeng Wang. 2017. Privacy Loss in Apple's Implementation of Differential Privacy on MacOS 10.12. (2017)."},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2007.12.020"}],"event":{"name":"APSys '19: 10th ACM SIGOPS Asia-Pacific Workshop on Systems","sponsor":["SIGOPS ACM Special Interest Group on Operating Systems"],"location":"Hangzhou China","acronym":"APSys '19"},"container-title":["Proceedings of the 10th ACM SIGOPS Asia-Pacific Workshop on Systems"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3343737.3343744","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3343737.3343744","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T23:44:24Z","timestamp":1750203864000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3343737.3343744"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,8,19]]},"references-count":36,"alternative-id":["10.1145\/3343737.3343744","10.1145\/3343737"],"URL":"https:\/\/doi.org\/10.1145\/3343737.3343744","relation":{},"subject":[],"published":{"date-parts":[[2019,8,19]]},"assertion":[{"value":"2019-08-19","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}