{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T07:25:18Z","timestamp":1776151518622,"version":"3.50.1"},"reference-count":35,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"6","license":[{"start":{"date-parts":[[2024,12,1]],"date-time":"2024-12-01T00:00:00Z","timestamp":1733011200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2024,12,1]],"date-time":"2024-12-01T00:00:00Z","timestamp":1733011200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,12,1]],"date-time":"2024-12-01T00:00:00Z","timestamp":1733011200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"National Key Research and Development Program of China","award":["2018AAA0101100"],"award-info":[{"award-number":["2018AAA0101100"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Big Data"],"published-print":{"date-parts":[[2024,12]]},"DOI":"10.1109\/tbdata.2022.3188292","type":"journal-article","created":{"date-parts":[[2022,7,11]],"date-time":"2022-07-11T19:48:43Z","timestamp":1657568923000},"page":"879-890","source":"Crossref","is-referenced-by-count":25,"title":["Privacy-Preserving Federated Adversarial Domain Adaptation Over Feature Groups for Interpretability"],"prefix":"10.1109","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2016-9503","authenticated-orcid":false,"given":"Yan","family":"Kang","sequence":"first","affiliation":[{"name":"AI Department, Webank, Shenzhen, Guangzhou, China"}]},{"given":"Yuanqin","family":"He","sequence":"additional","affiliation":[{"name":"Webank, Shenzhen, Guangdong, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5742-1330","authenticated-orcid":false,"given":"Jiahuan","family":"Luo","sequence":"additional","affiliation":[{"name":"Webank, Shenzhen, Guangdong, China"}]},{"given":"Tao","family":"Fan","sequence":"additional","affiliation":[{"name":"AI Department, Webank, Shenzhen, Guangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3800-3533","authenticated-orcid":false,"given":"Yang","family":"Liu","sequence":"additional","affiliation":[{"name":"Institute for AI Industry Research (AIR), Tsinghua University, Beijing, China"}]},{"given":"Qiang","family":"Yang","sequence":"additional","affiliation":[{"name":"Computer Science and Engineering, Hong Kong University of Science and Technology, Clearwater Bay, Kowloon, Hong Kong"}]}],"member":"263","reference":[{"key":"ref1","first-page":"7786","article-title":"Towards robust interpretability with self-explaining neural networks","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Alvarez-Melis"},{"key":"ref2","volume-title":"Handbook of Methods of Applied Statistics","volume":"I","author":"Chakravarti","year":"1967"},{"key":"ref3","first-page":"8930","article-title":"This looks like that: Deep learning for interpretable image recognition","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Chen"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1097\/00003643-201406001-00333"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1145\/1866739.1866758"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1007\/11681878_14"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1561\/9781601988195"},{"key":"ref8","article-title":"Label inference attacks against vertical federated learning","volume-title":"Proc. 31st USENIX Secur. Symp.","author":"Fu"},{"key":"ref9","first-page":"1180","article-title":"Unsupervised domain adaptation by backpropagation","volume-title":"Proc. 32nd Int. Conf. Mach. Learn.","author":"Ganin"},{"key":"ref10","article-title":"Federated deep learning with Bayesian privacy","author":"Gu","year":"2021"},{"key":"ref11","article-title":"Private federated learning on vertically partitioned data via entity resolution and homomorphic encryption","author":"Hardy","year":"2017"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1145\/3359789.3359824"},{"key":"ref13","article-title":"NBDT: Neural-backed decision trees","author":"Ho","year":"2020"},{"key":"ref14","article-title":"FedCG: Leverage conditional GAN for protecting privacy and maintaining competitive performance in federated learning","author":"Kang","year":"2021"},{"key":"ref15","article-title":"Label leakage and protection in two-party split learning","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Li"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101765"},{"key":"ref17","article-title":"Defending label inference and backdoor attacks in vertical federated learning","author":"Liu","year":"2021"},{"key":"ref18","first-page":"4765","article-title":"A unified approach to interpreting model predictions","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Lundberg"},{"key":"ref19","article-title":"Communication-efficient learning of deep networks from decentralized data","author":"McMahan","year":"2016"},{"key":"ref20","first-page":"8114","article-title":"Agnostic federated learning","volume-title":"Proc. 36th Int. Conf. Mach. Learn.","author":"Mohri"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1016\/j.dsp.2017.10.011"},{"key":"ref22","article-title":"Federated adversarial domain adaptation","author":"Peng","year":"2019"},{"key":"ref23","article-title":"Advances and open problems in federated learning","author":"Kairouz","year":"2019"},{"key":"ref24","article-title":"Private federated learning with domain adaptation","author":"Peterson","year":"2019"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00392"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1002\/9781119183471"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3014264"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.316"},{"key":"ref29","article-title":"No peek: A survey of private distributed deep learning","author":"Vepakomma","year":"2018"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01155"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1145\/3298981"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/547"},{"key":"ref33","article-title":"Additively homomorphical encryption based deep neural network for asymmetrically collaborative machine learning","author":"Zhang","year":"2020"},{"key":"ref34","first-page":"7404","article-title":"Bridging theory and algorithm for domain adaptation","volume-title":"Proc. 36th Int. Conf. Mach. Learn.","author":"Zhang"},{"key":"ref35","first-page":"14774","article-title":"Deep leakage from gradients","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Zhu"}],"container-title":["IEEE Transactions on Big Data"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6687317\/10750532\/09826576.pdf?arnumber=9826576","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,27]],"date-time":"2024-11-27T15:23:51Z","timestamp":1732721031000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9826576\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12]]},"references-count":35,"journal-issue":{"issue":"6"},"URL":"https:\/\/doi.org\/10.1109\/tbdata.2022.3188292","relation":{},"ISSN":["2332-7790","2372-2096"],"issn-type":[{"value":"2332-7790","type":"electronic"},{"value":"2372-2096","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12]]}}}