{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:13:26Z","timestamp":1750220006826,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":11,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,11,7]],"date-time":"2022-11-07T00:00:00Z","timestamp":1667779200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,11,7]]},"DOI":"10.1145\/3548606.3563480","type":"proceedings-article","created":{"date-parts":[[2022,11,7]],"date-time":"2022-11-07T11:41:28Z","timestamp":1667821288000},"page":"3523-3525","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Demo -- MaLFraDA"],"prefix":"10.1145","author":[{"given":"Chandra","family":"Thapa","sequence":"first","affiliation":[{"name":"CSIRO's Data61, Marsfield, NSW, Australia"}]},{"given":"Seyit","family":"Camtepe","sequence":"additional","affiliation":[{"name":"CSIRO's Data61, Marsfield, NSW, Australia"}]},{"given":"Raj","family":"Gaire","sequence":"additional","affiliation":[{"name":"KPMG Australia, Canberra, ACT, Australia"}]},{"given":"Surya","family":"Nepal","sequence":"additional","affiliation":[{"name":"CSIRO's Data61, Marsfield, NSW, Australia"}]},{"given":"Seung Ick","family":"Jang","sequence":"additional","affiliation":[{"name":"CSIRO's Data61, Marsfield, NSW, Australia"}]}],"member":"320","published-online":{"date-parts":[[2022,11,7]]},"reference":[{"volume-title":"Argo Workflows: Kubernetes-native workflow engine supporting DAG and Step-based workflows. https:\/\/argoproj.github.io\/workflows\/.","year":"2022","key":"e_1_3_2_1_1_1","unstructured":"Argo. 2022 . Argo Workflows: Kubernetes-native workflow engine supporting DAG and Step-based workflows. https:\/\/argoproj.github.io\/workflows\/. Argo. 2022. Argo Workflows: Kubernetes-native workflow engine supporting DAG and Step-based workflows. https:\/\/argoproj.github.io\/workflows\/."},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jnca.2018.05.003"},{"key":"e_1_3_2_1_4_1","volume-title":"Advances and Open Problems in Federated Learning. Foundations and Trends\u00ae in Machine Learning","volume":"14","author":"Kairouz Peter","year":"2021","unstructured":"Peter Kairouz , H. Brendan McMahan, Brendan Avent, Aur\u00e9lien Bellet, and et al. 2021 . Advances and Open Problems in Federated Learning. Foundations and Trends\u00ae in Machine Learning , Vol. 14 , 1--2 ( 2021 ), 1--210. Peter Kairouz, H. Brendan McMahan, Brendan Avent, Aur\u00e9lien Bellet, and et al. 2021. Advances and Open Problems in Federated Learning. Foundations and Trends\u00ae in Machine Learning, Vol. 14, 1--2 (2021), 1--210."},{"key":"e_1_3_2_1_5_1","volume-title":"Federated Optimization: Distributed Machine Learning for On-Device Intelligence. arxiv","author":"Jakub Konecn\u00fd","year":"2016","unstructured":"Jakub Konecn\u00fd , H. Brendan McMahan , Daniel Ramage , and Peter Richt\u00e1rik . 2016 . Federated Optimization: Distributed Machine Learning for On-Device Intelligence. arxiv (2016). http:\/\/arxiv.org\/abs\/1610.02527. Jakub Konecn\u00fd , H. Brendan McMahan, Daniel Ramage, and Peter Richt\u00e1rik. 2016. Federated Optimization: Distributed Machine Learning for On-Device Intelligence. arxiv (2016). http:\/\/arxiv.org\/abs\/1610.02527."},{"key":"e_1_3_2_1_6_1","unstructured":"Kubernetes. 2022. What is Kubernetes? https:\/\/kubernetes.io\/docs\/concepts\/overview\/what-is-kubernetes\/.  Kubernetes. 2022. What is Kubernetes? https:\/\/kubernetes.io\/docs\/concepts\/overview\/what-is-kubernetes\/."},{"key":"e_1_3_2_1_7_1","volume-title":"ACM Comput. Surv.","volume":"54","author":"Liu Bo","year":"2021","unstructured":"Bo Liu , Ming Ding , Sina Shaham , Wenny Rahayu , Farhad Farokhi , and Zihuai Lin . 2021 . When Machine Learning Meets Privacy: A Survey and Outlook . ACM Comput. Surv. , Vol. 54 , 2, Article 31 (mar 2021), 36 pages. Bo Liu, Ming Ding, Sina Shaham, Wenny Rahayu, Farhad Farokhi, and Zihuai Lin. 2021. When Machine Learning Meets Privacy: A Survey and Outlook. ACM Comput. Surv., Vol. 54, 2, Article 31 (mar 2021), 36 pages."},{"key":"e_1_3_2_1_8_1","unstructured":"MongoDB. 2022. MongoDB Documentation. https:\/\/www.mongodb.com\/docs\/.  MongoDB. 2022. MongoDB Documentation. https:\/\/www.mongodb.com\/docs\/."},{"key":"e_1_3_2_1_9_1","unstructured":"NodeJS. 2022. Documentation. https:\/\/nodejs.org\/en\/docs\/  NodeJS. 2022. Documentation. https:\/\/nodejs.org\/en\/docs\/"},{"key":"e_1_3_2_1_11_1","unstructured":"RabbitMQ. 2022. RabbitMQ Tutorials. https:\/\/www.rabbitmq.com\/getstarted.html.  RabbitMQ. 2022. RabbitMQ Tutorials. https:\/\/www.rabbitmq.com\/getstarted.html."},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i8.20825"},{"key":"e_1_3_2_1_13_1","unstructured":"zdnet.com. 2018. AFP analysing sensitive data without accessing the unpleasant truth. https:\/\/www.zdnet.com\/article\/afp-analysing-sensitive-data-without-accessing-the-unpleasant-truth.  zdnet.com. 2018. AFP analysing sensitive data without accessing the unpleasant truth. https:\/\/www.zdnet.com\/article\/afp-analysing-sensitive-data-without-accessing-the-unpleasant-truth."}],"event":{"name":"CCS '22: 2022 ACM SIGSAC Conference on Computer and Communications Security","sponsor":["SIGSAC ACM Special Interest Group on Security, Audit, and Control"],"location":"Los Angeles CA USA","acronym":"CCS '22"},"container-title":["Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3548606.3563480","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3548606.3563480","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T17:51:06Z","timestamp":1750182666000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3548606.3563480"}},"subtitle":["A Machine Learning Framework with Data Airlock"],"short-title":[],"issued":{"date-parts":[[2022,11,7]]},"references-count":11,"alternative-id":["10.1145\/3548606.3563480","10.1145\/3548606"],"URL":"https:\/\/doi.org\/10.1145\/3548606.3563480","relation":{},"subject":[],"published":{"date-parts":[[2022,11,7]]},"assertion":[{"value":"2022-11-07","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}