{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T16:03:36Z","timestamp":1778601816361,"version":"3.51.4"},"publisher-location":"New York, NY, USA","reference-count":55,"publisher":"ACM","license":[{"start":{"date-parts":[[2019,11,3]],"date-time":"2019-11-03T00:00:00Z","timestamp":1572739200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"National Science Foundation of China (NSFC)","award":["U1811463"],"award-info":[{"award-number":["U1811463"]}]},{"name":"National Science Foundation of China (NSFC)","award":["61532004"],"award-info":[{"award-number":["61532004"]}]},{"name":"National Science Foundation of China (NSFC)","award":["61822201"],"award-info":[{"award-number":["61822201"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2019,11,3]]},"DOI":"10.1145\/3357384.3357909","type":"proceedings-article","created":{"date-parts":[[2019,11,4]],"date-time":"2019-11-04T14:11:35Z","timestamp":1572876695000},"page":"1071-1080","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":28,"title":["Federated Topic Modeling"],"prefix":"10.1145","author":[{"given":"Di","family":"Jiang","sequence":"first","affiliation":[{"name":"WeBank Co., Ltd, Shenzhen, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuanfeng","family":"Song","sequence":"additional","affiliation":[{"name":"WeBank Co., Ltd, Shenzhen; Hong Kong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yongxin","family":"Tong","sequence":"additional","affiliation":[{"name":"Beihang University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xueyang","family":"Wu","sequence":"additional","affiliation":[{"name":"The Hong Kong University of Science and Technology, Hong Kong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weiwei","family":"Zhao","sequence":"additional","affiliation":[{"name":"WeBank Co., Ltd, Shenzhen, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qian","family":"Xu","sequence":"additional","affiliation":[{"name":"WeBank Co., Ltd, Shenzhen, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qiang","family":"Yang","sequence":"additional","affiliation":[{"name":"WeBank Co., Ltd, Shenzhen; Hong Kong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2019,11,3]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/2348283.2348454"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/2911451.2914714"},{"key":"e_1_3_2_1_3_1","volume-title":"Shrinkwrap: Differentially-Private Query Processing in Private Data Federations. arXiv preprint arXiv:1810.01816","author":"Bater Johes","year":"2018","unstructured":"Johes Bater , Xi He , William Ehrich , Ashwin Machanavajjhala , and Jennie Rogers . 2018 . Shrinkwrap: Differentially-Private Query Processing in Private Data Federations. arXiv preprint arXiv:1810.01816 (2018). Johes Bater, Xi He, William Ehrich, Ashwin Machanavajjhala, and Jennie Rogers. 2018. Shrinkwrap: Differentially-Private Query Processing in Private Data Federations. arXiv preprint arXiv:1810.01816 (2018)."},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.5555\/944919.944937"},{"key":"e_1_3_2_1_5_1","volume-title":"Practical secure aggregation for federated learning on user-held data. arXiv preprint arXiv:1611.04482","author":"Bonawitz Keith","year":"2016","unstructured":"Keith Bonawitz , Vladimir Ivanov , Ben Kreuter , Antonio Marcedone , H Brendan McMahan , Sarvar Patel , Daniel Ramage , Aaron Segal , and Karn Seth . 2016. Practical secure aggregation for federated learning on user-held data. arXiv preprint arXiv:1611.04482 ( 2016 ). Keith Bonawitz, Vladimir Ivanov, Ben Kreuter, Antonio Marcedone, H Brendan McMahan, Sarvar Patel, Daniel Ramage, Aaron Segal, and Karn Seth. 2016. Practical secure aggregation for federated learning on user-held data. arXiv preprint arXiv:1611.04482 (2016)."},{"key":"e_1_3_2_1_6_1","volume-title":"Ioannis Ch Paschalidis, and Wei Shi","author":"Brisimi Theodora S","year":"2018","unstructured":"Theodora S Brisimi , Ruidi Chen , Theofanie Mela , Alex Olshevsky , Ioannis Ch Paschalidis, and Wei Shi . 2018 . Federated learning of predictive models from federated Electronic Health Records. International journal of medical informatics , Vol. 112 (2018), 59--67. Theodora S Brisimi, Ruidi Chen, Theofanie Mela, Alex Olshevsky, Ioannis Ch Paschalidis, and Wei Shi. 2018. Federated learning of predictive models from federated Electronic Health Records. International journal of medical informatics , Vol. 112 (2018), 59--67."},{"key":"e_1_3_2_1_7_1","volume-title":"Data protection: a practical guide to UK and EU law","author":"Carey Peter","unstructured":"Peter Carey . 2018. Data protection: a practical guide to UK and EU law . Oxford University Press, Inc. Peter Carey. 2018. Data protection: a practical guide to UK and EU law .Oxford University Press, Inc."},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/1871437.1871745"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2010.5494942"},{"key":"e_1_3_2_1_10_1","volume-title":"SecureBoost: A Lossless Federated Learning Framework. CoRR","author":"Cheng Kewei","year":"2019","unstructured":"Kewei Cheng , Tao Fan , Yilun Jin , Yang Liu , Tianjian Chen , and Qiang Yang . 2019. SecureBoost: A Lossless Federated Learning Framework. CoRR , Vol. abs\/ 1901 .08755 ( 2019 ). arxiv: 1901.08755 http:\/\/arxiv.org\/abs\/1901.08755 Kewei Cheng, Tao Fan, Yilun Jin, Yang Liu, Tianjian Chen, and Qiang Yang. 2019. SecureBoost: A Lossless Federated Learning Framework. CoRR , Vol. abs\/1901.08755 (2019). arxiv: 1901.08755 http:\/\/arxiv.org\/abs\/1901.08755"},{"key":"e_1_3_2_1_11_1","volume-title":"5th International Conference, TAMC 2008, Xi'an, China, April 25--29, 2008. Proceedings. 1--19","author":"Dwork Cynthia","year":"2008","unstructured":"Cynthia Dwork . 2008 . Differential Privacy: A Survey of Results. In Theory and Applications of Models of Computation , 5th International Conference, TAMC 2008, Xi'an, China, April 25--29, 2008. Proceedings. 1--19 . Cynthia Dwork. 2008. Differential Privacy: A Survey of Results. In Theory and Applications of Models of Computation, 5th International Conference, TAMC 2008, Xi'an, China, April 25--29, 2008. Proceedings. 1--19."},{"key":"e_1_3_2_1_12_1","volume-title":"et almbox","author":"Dwork Cynthia","year":"2014","unstructured":"Cynthia Dwork , Aaron Roth , et almbox . 2014 . The algorithmic foundations of differential privacy. Foundations and Trends\u00ae in Theoretical Computer Science , Vol. 9 , 3--4 (2014), 211--407. Cynthia Dwork, Aaron Roth, et almbox. 2014. The algorithmic foundations of differential privacy. Foundations and Trends\u00ae in Theoretical Computer Science , Vol. 9, 3--4 (2014), 211--407."},{"key":"e_1_3_2_1_13_1","volume-title":"On the theory and practice of privacy-preserving Bayesian data analysis. arXiv preprint arXiv:1603.07294","author":"Foulds James","year":"2016","unstructured":"James Foulds , Joseph Geumlek , Max Welling , and Kamalika Chaudhuri . 2016. On the theory and practice of privacy-preserving Bayesian data analysis. arXiv preprint arXiv:1603.07294 ( 2016 ). James Foulds, Joseph Geumlek, Max Welling, and Kamalika Chaudhuri. 2016. On the theory and practice of privacy-preserving Bayesian data analysis. arXiv preprint arXiv:1603.07294 (2016)."},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/116873.116878"},{"key":"e_1_3_2_1_15_1","volume-title":"Differentially private federated learning: A client level perspective. arXiv preprint arXiv:1712.07557","author":"Geyer Robin C","year":"2017","unstructured":"Robin C Geyer , Tassilo Klein , and Moin Nabi . 2017. Differentially private federated learning: A client level perspective. arXiv preprint arXiv:1712.07557 ( 2017 ). Robin C Geyer, Tassilo Klein, and Moin Nabi. 2017. Differentially private federated learning: A client level perspective. arXiv preprint arXiv:1712.07557 (2017)."},{"key":"e_1_3_2_1_16_1","volume-title":"Markov chain Monte Carlo in practice","author":"Gilks Walter R","unstructured":"Walter R Gilks , Sylvia Richardson , and David Spiegelhalter . 1995. Markov chain Monte Carlo in practice . Chapman and Hall\/CRC. Walter R Gilks, Sylvia Richardson, and David Spiegelhalter. 1995. Markov chain Monte Carlo in practice .Chapman and Hall\/CRC."},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.0307752101"},{"key":"e_1_3_2_1_18_1","volume-title":"Privacy-preserving Transfer Learning for Knowledge Sharing. arXiv preprint arXiv:1811.09491","author":"Guo Xiawei","year":"2018","unstructured":"Xiawei Guo , Quanming Yao , WeiWei Tu , Yuqiang Chen , Wenyuan Dai , and Qiang Yang . 2018. Privacy-preserving Transfer Learning for Knowledge Sharing. arXiv preprint arXiv:1811.09491 ( 2018 ). Xiawei Guo, Quanming Yao, WeiWei Tu, Yuqiang Chen, Wenyuan Dai, and Qiang Yang. 2018. Privacy-preserving Transfer Learning for Knowledge Sharing. arXiv preprint arXiv:1811.09491 (2018)."},{"key":"e_1_3_2_1_19_1","volume-title":"International Conference on Machine Learning . 555--563","author":"Hamm Jihun","year":"2016","unstructured":"Jihun Hamm , Yingjun Cao , and Mikhail Belkin . 2016 . Learning privately from multiparty data . In International Conference on Machine Learning . 555--563 . Jihun Hamm, Yingjun Cao, and Mikhail Belkin. 2016. Learning privately from multiparty data. In International Conference on Machine Learning . 555--563."},{"key":"e_1_3_2_1_20_1","volume-title":"Sean Augenstein, Hubert Eichner, Chlo\u00e9 Kiddon, and Daniel Ramage.","author":"Hard Andrew","year":"2018","unstructured":"Andrew Hard , Kanishka Rao , Rajiv Mathews , Francc oise Beaufays , Sean Augenstein, Hubert Eichner, Chlo\u00e9 Kiddon, and Daniel Ramage. 2018 a. Federated learning for mobile keyboard prediction. arXiv preprint arXiv:1811.03604 (2018). Andrew Hard, Kanishka Rao, Rajiv Mathews, Francc oise Beaufays, Sean Augenstein, Hubert Eichner, Chlo\u00e9 Kiddon, and Daniel Ramage. 2018a. Federated learning for mobile keyboard prediction. arXiv preprint arXiv:1811.03604 (2018)."},{"key":"e_1_3_2_1_21_1","volume-title":"Sean Augenstein, Hubert Eichner, Chlo\u00e9 Kiddon, and Daniel Ramage.","author":"Hard Andrew","year":"2018","unstructured":"Andrew Hard , Kanishka Rao , Rajiv Mathews , Francc oise Beaufays , Sean Augenstein, Hubert Eichner, Chlo\u00e9 Kiddon, and Daniel Ramage. 2018 b. Federated learning for mobile keyboard prediction. arXiv preprint arXiv:1811.03604 (2018). Andrew Hard, Kanishka Rao, Rajiv Mathews, Francc oise Beaufays, Sean Augenstein, Hubert Eichner, Chlo\u00e9 Kiddon, and Daniel Ramage. 2018b. Federated learning for mobile keyboard prediction. arXiv preprint arXiv:1811.03604 (2018)."},{"key":"e_1_3_2_1_22_1","volume-title":"Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption. arXiv preprint arXiv:1711.10677","author":"Hardy Stephen","year":"2017","unstructured":"Stephen Hardy , Wilko Henecka , Hamish Ivey-Law , Richard Nock , Giorgio Patrini , Guillaume Smith , and Brian Thorne . 2017. Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption. arXiv preprint arXiv:1711.10677 ( 2017 ). Stephen Hardy, Wilko Henecka, Hamish Ivey-Law, Richard Nock, Giorgio Patrini, Guillaume Smith, and Brian Thorne. 2017. Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption. arXiv preprint arXiv:1711.10677 (2017)."},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/2505515.2505642"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-37487-6_18"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/1935826.1935932"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1109\/NAFIPS-WConSC.2015.7284190"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0167-6393(01)00041-3"},{"key":"e_1_3_2_1_28_1","volume-title":"Federated learning for keyword spotting. arXiv preprint arXiv:1810.05512","author":"Leroy David","year":"2018","unstructured":"David Leroy , Alice Coucke , Thibaut Lavril , Thibault Gisselbrecht , and Joseph Dureau . 2018. Federated learning for keyword spotting. arXiv preprint arXiv:1810.05512 ( 2018 ). David Leroy, Alice Coucke, Thibaut Lavril, Thibault Gisselbrecht, and Joseph Dureau. 2018. Federated learning for keyword spotting. arXiv preprint arXiv:1810.05512 (2018)."},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1145\/2623330.2623756"},{"key":"e_1_3_2_1_30_1","volume-title":"Meta-sgd: Learning to learn quickly for few shot learning. arXiv preprint arXiv:1707.09835","author":"Li Zhenguo","year":"2017","unstructured":"Zhenguo Li , Fengwei Zhou , Fei Chen , and Hang Li . 2017 . Meta-sgd: Learning to learn quickly for few shot learning. arXiv preprint arXiv:1707.09835 (2017). Zhenguo Li, Fengwei Zhou, Fei Chen, and Hang Li. 2017. Meta-sgd: Learning to learn quickly for few shot learning. arXiv preprint arXiv:1707.09835 (2017)."},{"key":"e_1_3_2_1_31_1","volume-title":"Secure Federated Transfer Learning. CoRR","author":"Liu Yang","year":"2018","unstructured":"Yang Liu , Tianjian Chen , and Qiang Yang . 2018. Secure Federated Transfer Learning. CoRR , Vol. abs\/ 1812 .03337 ( 2018 ). arxiv: 1812.03337 http:\/\/arxiv.org\/abs\/1812.03337 Yang Liu, Tianjian Chen, and Qiang Yang. 2018. Secure Federated Transfer Learning. CoRR , Vol. abs\/1812.03337 (2018). arxiv: 1812.03337 http:\/\/arxiv.org\/abs\/1812.03337"},{"key":"e_1_3_2_1_32_1","unstructured":"Jon D Mcauliffe and David M Blei. 2008. Supervised topic models. In Advances in neural information processing systems. 121--128.  Jon D Mcauliffe and David M Blei. 2008. Supervised topic models. In Advances in neural information processing systems. 121--128."},{"key":"e_1_3_2_1_33_1","volume-title":"et almbox","author":"McMahan H Brendan","year":"2016","unstructured":"H Brendan McMahan , Eider Moore , Daniel Ramage , Seth Hampson , et almbox . 2016 . Communication-efficient learning of deep networks from decentralized data. arXiv preprint arXiv:1602.05629 (2016). H Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, et almbox. 2016. Communication-efficient learning of deep networks from decentralized data. arXiv preprint arXiv:1602.05629 (2016)."},{"key":"e_1_3_2_1_34_1","volume-title":"Emiliano De Cristofaro, and Vitaly Shmatikov","author":"Melis Luca","year":"2018","unstructured":"Luca Melis , Congzheng Song , Emiliano De Cristofaro, and Vitaly Shmatikov . 2018 . Exploiting unintended feature leakage in collaborative learning. In Exploiting Unintended Feature Leakage in Collaborative Learning. IEEE , 0. Luca Melis, Congzheng Song, Emiliano De Cristofaro, and Vitaly Shmatikov. 2018. Exploiting unintended feature leakage in collaborative learning. In Exploiting Unintended Feature Leakage in Collaborative Learning. IEEE, 0."},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.5555\/1577069.1755845"},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-10439-8_28"},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2009.191"},{"key":"e_1_3_2_1_38_1","volume-title":"Semi-supervised knowledge transfer for deep learning from private training data. arXiv preprint arXiv:1610.05755","author":"Papernot Nicolas","year":"2016","unstructured":"Nicolas Papernot , Mart'in Abadi , Ulfar Erlingsson , Ian Goodfellow , and Kunal Talwar . 2016. Semi-supervised knowledge transfer for deep learning from private training data. arXiv preprint arXiv:1610.05755 ( 2016 ). Nicolas Papernot, Mart'in Abadi, Ulfar Erlingsson, Ian Goodfellow, and Kunal Talwar. 2016. Semi-supervised knowledge transfer for deep learning from private training data. arXiv preprint arXiv:1610.05755 (2016)."},{"key":"e_1_3_2_1_39_1","volume-title":"Private topic modeling. arXiv preprint arXiv:1609.04120","author":"Park Mijung","year":"2016","unstructured":"Mijung Park , James Foulds , Kamalika Chaudhuri , and Max Welling . 2016. Private topic modeling. arXiv preprint arXiv:1609.04120 ( 2016 ). Mijung Park, James Foulds, Kamalika Chaudhuri, and Max Welling. 2016. Private topic modeling. arXiv preprint arXiv:1609.04120 (2016)."},{"key":"e_1_3_2_1_40_1","volume-title":"et almbox","author":"Rivest Ronald L","year":"1978","unstructured":"Ronald L Rivest , Len Adleman , Michael L Dertouzos , et almbox . 1978 . On data banks and privacy homomorphisms. Foundations of secure computation , Vol. 4 , 11 (1978), 169--180. Ronald L Rivest, Len Adleman, Michael L Dertouzos, et almbox. 1978. On data banks and privacy homomorphisms. Foundations of secure computation , Vol. 4, 11 (1978), 169--180."},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1214\/12-AOAS618"},{"key":"e_1_3_2_1_42_1","first-page":"513","article-title":"The EU general data protection regulation: Toward a property regime for protecting data privacy","volume":"123","author":"Victor Jacob M","year":"2013","unstructured":"Jacob M Victor . 2013 . The EU general data protection regulation: Toward a property regime for protecting data privacy . Yale LJ , Vol. 123 (2013), 513 . Jacob M Victor. 2013. The EU general data protection regulation: Toward a property regime for protecting data privacy. Yale LJ , Vol. 123 (2013), 513.","journal-title":"Yale LJ"},{"key":"e_1_3_2_1_43_1","first-page":"221","article-title":"European union data privacy law reform: General data protection regulation, privacy shield, and the right to delisting","volume":"72","author":"Voss W Gregory","year":"2016","unstructured":"W Gregory Voss . 2016 . European union data privacy law reform: General data protection regulation, privacy shield, and the right to delisting . Business Lawyer , Vol. 72 , 1 (2016), 221 -- 233 . W Gregory Voss. 2016. European union data privacy law reform: General data protection regulation, privacy shield, and the right to delisting. Business Lawyer , Vol. 72, 1 (2016), 221--233.","journal-title":"Business Lawyer"},{"key":"e_1_3_2_1_44_1","volume-title":"Adaptive federated learning in resource constrained edge computing systems. learning","author":"Wang Shiqiang","year":"2018","unstructured":"Shiqiang Wang , Tiffany Tuor , Theodoros Salonidis , Kin K Leung , Christian Makaya , Ting He , and Kevin Chan . 2018c. Adaptive federated learning in resource constrained edge computing systems. learning , Vol. 8 ( 2018 ), 9. Shiqiang Wang, Tiffany Tuor, Theodoros Salonidis, Kin K Leung, Christian Makaya, Ting He, and Kevin Chan. 2018c. Adaptive federated learning in resource constrained edge computing systems. learning , Vol. 8 (2018), 9."},{"key":"e_1_3_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1145\/1150402.1150450"},{"key":"e_1_3_2_1_46_1","volume-title":"Differentially Private Hypothesis Transfer Learning. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer, 811--826","author":"Wang Yang","year":"2018","unstructured":"Yang Wang , Quanquan Gu , and Donald Brown . 2018 a. Differentially Private Hypothesis Transfer Learning. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer, 811--826 . Yang Wang, Quanquan Gu, and Donald Brown. 2018a. Differentially Private Hypothesis Transfer Learning. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer, 811--826."},{"key":"e_1_3_2_1_47_1","first-page":"2493","article-title":"Privacy for Free: Posterior Sampling and Stochastic Gradient Monte Carlo","volume":"15","author":"Wang Yu-Xiang","year":"2015","unstructured":"Yu-Xiang Wang , Stephen E Fienberg , and Alexander J Smola . 2015 . Privacy for Free: Posterior Sampling and Stochastic Gradient Monte Carlo .. In ICML , Vol. 15. 2493 -- 2502 . Yu-Xiang Wang, Stephen E Fienberg, and Alexander J Smola. 2015. Privacy for Free: Posterior Sampling and Stochastic Gradient Monte Carlo.. In ICML , Vol. 15. 2493--2502.","journal-title":"ICML"},{"key":"e_1_3_2_1_48_1","volume-title":"Beyond Inferring Class Representatives: User-Level Privacy Leakage From Federated Learning. arXiv preprint arXiv:1812.00535","author":"Wang Zhibo","year":"2018","unstructured":"Zhibo Wang , Mengkai Song , Zhifei Zhang , Yang Song , Qian Wang , and Hairong Qi. 2018b. Beyond Inferring Class Representatives: User-Level Privacy Leakage From Federated Learning. arXiv preprint arXiv:1812.00535 ( 2018 ). Zhibo Wang, Mengkai Song, Zhifei Zhang, Yang Song, Qian Wang, and Hairong Qi. 2018b. Beyond Inferring Class Representatives: User-Level Privacy Leakage From Federated Learning. arXiv preprint arXiv:1812.00535 (2018)."},{"key":"e_1_3_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1109\/SLT.2014.7078615"},{"key":"e_1_3_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.1145\/3298981"},{"key":"e_1_3_2_1_51_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939821"},{"key":"e_1_3_2_1_52_1","first-page":"160","article-title":"Protocols for secure computations","volume":"82","author":"Chi-Chih Yao Andrew","year":"1982","unstructured":"Andrew Chi-Chih Yao . 1982 . Protocols for secure computations . In FOCS , Vol. 82. 160 -- 164 . Andrew Chi-Chih Yao. 1982. Protocols for secure computations. In FOCS, Vol. 82. 160--164.","journal-title":"FOCS"},{"key":"e_1_3_2_1_53_1","volume-title":"AUTOMATIC SPEECH RECOGNITION","author":"Yu Dong","unstructured":"Dong Yu and Li Deng . 2016. AUTOMATIC SPEECH RECOGNITION . Springer . Dong Yu and Li Deng. 2016. AUTOMATIC SPEECH RECOGNITION. Springer."},{"key":"e_1_3_2_1_54_1","doi-asserted-by":"publisher","DOI":"10.1145\/2736277.2741115"},{"key":"e_1_3_2_1_55_1","doi-asserted-by":"publisher","DOI":"10.1145\/2187836.2187955"}],"event":{"name":"CIKM '19: The 28th ACM International Conference on Information and Knowledge Management","location":"Beijing China","acronym":"CIKM '19","sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web","SIGIR ACM Special Interest Group on Information Retrieval"]},"container-title":["Proceedings of the 28th ACM International Conference on Information and Knowledge Management"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3357384.3357909","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3357384.3357909","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T23:23:02Z","timestamp":1750202582000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3357384.3357909"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,11,3]]},"references-count":55,"alternative-id":["10.1145\/3357384.3357909","10.1145\/3357384"],"URL":"https:\/\/doi.org\/10.1145\/3357384.3357909","relation":{},"subject":[],"published":{"date-parts":[[2019,11,3]]},"assertion":[{"value":"2019-11-03","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}