{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,2]],"date-time":"2026-03-02T22:12:05Z","timestamp":1772489525750,"version":"3.50.1"},"reference-count":66,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"3","license":[{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"am","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"crossref","award":["R01AG084236"],"award-info":[{"award-number":["R01AG084236"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"crossref","award":["R01AG083039"],"award-info":[{"award-number":["R01AG083039"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100000968","name":"American Heart Association","doi-asserted-by":"crossref","award":["19GPSGC35180031"],"award-info":[{"award-number":["19GPSGC35180031"]}],"id":[{"id":"10.13039\/100000968","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["1920920"],"award-info":[{"award-number":["1920920"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["2125872"],"award-info":[{"award-number":["2125872"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["2223793"],"award-info":[{"award-number":["2223793"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Artif. Intell."],"published-print":{"date-parts":[[2026,3]]},"DOI":"10.1109\/tai.2025.3593470","type":"journal-article","created":{"date-parts":[[2025,7,29]],"date-time":"2025-07-29T18:32:57Z","timestamp":1753813977000},"page":"1273-1289","source":"Crossref","is-referenced-by-count":0,"title":["Teleportation: Defense Against Stealing Attacks of Data-Driven Healthcare APIs"],"prefix":"10.1109","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4323-642X","authenticated-orcid":false,"given":"Tiehang","family":"Duan","sequence":"first","affiliation":[{"name":"Department of Computer Science, College of Computing, Grand Valley State University, Allendale, MI, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2780-9446","authenticated-orcid":false,"given":"Zhenyi","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Maryland, College Park, MD, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5659-3464","authenticated-orcid":false,"given":"Li","family":"Shen","sequence":"additional","affiliation":[{"name":"School of Cyber Science and Technology, Sun Yat-sen University, Shenzhen, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6955-4445","authenticated-orcid":false,"given":"Siyu","family":"Luan","sequence":"additional","affiliation":[{"name":"Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Frederiksberg, Denmark"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8865-7717","authenticated-orcid":false,"given":"Fang","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Artificial Intelligence and Informatics, Mayo Clinic, Jacksonville, FL, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8921-6646","authenticated-orcid":false,"given":"Gianfranco","family":"Doretto","sequence":"additional","affiliation":[{"name":"Lane Department of Computer Science and Electrical Engineering, West Virginia University, Morgantown, WV, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7982-4744","authenticated-orcid":false,"given":"Donald A.","family":"Adjeroh","sequence":"additional","affiliation":[{"name":"Lane Department of Computer Science and Electrical Engineering, West Virginia University, Morgantown, WV, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1069-9236","authenticated-orcid":false,"given":"Shuteng","family":"Niu","sequence":"additional","affiliation":[{"name":"Department of Artificial Intelligence and Informatics, Mayo Clinic, Jacksonville, FL, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9949-7007","authenticated-orcid":false,"given":"Jianfu","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Artificial Intelligence and Informatics, Mayo Clinic, Jacksonville, FL, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4267-1924","authenticated-orcid":false,"given":"Cui","family":"Tao","sequence":"additional","affiliation":[{"name":"Department of Artificial Intelligence and Informatics, Mayo Clinic, Jacksonville, FL, USA"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1145\/3339252.3340332"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCPCT.2017.8074287"},{"key":"ref3","article-title":"Exploring data breaches and means to mitigate future\n                        occurrences in healthcare institutions: A content\n                    analysis","author":"Wickham","year":"2019"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00474"},{"key":"ref5","article-title":"Zero-shot knowledge distillation from a\n                        decision-based black-box model","author":"Wang","year":"2021"},{"key":"ref6","first-page":"109","article-title":"Protecting DNNs from theft using an ensemble of\n                        diverse models","volume-title":"Proc. Int. Conf. Learn.\n                        Representations","author":"Kariyappa","year":"2021"},{"key":"ref7","article-title":"How to steer your adversary: Targeted and efficient\n                        model stealing defenses with gradient redirection","author":"Mazeika","year":"2022"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00085"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00474"},{"key":"ref10","article-title":"Scalable extraction of\n                        training data from (production) language models","volume-title":"The Thirteenth International Conference on Learning\n                        Representations","author":"Nasr","year":"2023"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1016\/j.cosrev.2019.100199"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2018.07.023"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1504\/ijsn.2015.071829"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2019.2925071"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2018.2849014"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2022.3156268"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-59719-1_34"},{"key":"ref18","article-title":"Out of distribution detection and adversarial attacks\n                        on deep neural networks for robust medical image\n                    analysis","volume-title":"ICML Workshop Adversarial Mach.\n                        Learn.","author":"Uwimana","year":"2021"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1145\/3436755"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1145\/3133956.3134077"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2024.106338"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2021.3138919"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2022.06.022"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1145\/3528580.3532845"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1177\/20552076231177144"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/ICIT52682.2021.9491691"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2022.3179429"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/TSC.2018.2790943"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-59354-4_5"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-28954-6_7"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00509"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.5555\/3241094.3241142"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1145\/3052973.3053009"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i01.5432"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01360"},{"key":"ref36","first-page":"314","article-title":"Learning to learn from APIs: Black-box data-free\n                        meta-learning","volume-title":"Proc. 40th Int. Conf.\n                        Mach. Learn. (ICML)","author":"Hu","year":"2023"},{"key":"ref37","first-page":"10675","article-title":"Zero-shot knowledge distillation from a\n                        decision-based black-box model","volume-title":"Proc.\n                        38th Int. Conf. Mach. Learn. (PMLR)","volume":"139","author":"Wang","year":"2021"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01485"},{"key":"ref39","first-page":"78","article-title":"Prediction poisoning: Towards defenses against DNN\n                        model stealing attacks","volume-title":"Proc. Int. Conf.\n                        Learn. Representations","author":"Orekondy","year":"2020"},{"key":"ref40","first-page":"624","article-title":"Defending against data-free\n                        model extraction by distributionally robust defensive\n                        training","volume-title":"Proc. Adv. Neural Inf. Process.\n                        Syst.","volume":"36","author":"Wang","year":"2023"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.3233\/faia230318"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/EuroSP.2019.00044"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2020.3003837"},{"key":"ref44","first-page":"35","article-title":"Variational inference with tail-adaptive\n                        F-divergence","volume-title":"Proc. Adv. Neural Inf.\n                        Process. Syst.","volume":"31","author":"Wang","year":"2018"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1038\/sdata.2016.35"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1093\/jamia\/ocab221"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1038\/s41597-019-0103-9"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1145\/3581783.3612092"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1016\/j.amsu.2020.09.044"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1161\/JAHA.123.029900"},{"key":"ref51","article-title":"Unsupervised representation learning with deep\n                        convolutional generative adversarial networks","author":"Radford","year":"2016"},{"key":"ref52","article-title":"Lecture 6.5-: Divide the gradient by a running\n                        average of its recent magnitude","author":"Tieleman","year":"2012"},{"key":"ref53","article-title":"Scalable extraction of\n                        training data from (production) language models","author":"Nasr","year":"2023"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/SP46214.2022.9833649"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2017.41"},{"key":"ref56","first-page":"2633","article-title":"Extracting training data from\n                        large language models","volume-title":"Proc. 30th USENIX\n                        Secur. Symp. (USENIX Secur.)","author":"Carlini","year":"2021"},{"key":"ref57","first-page":"267","article-title":"The secret sharer: Evaluating and testing unintended\n                        memorization in neural networks","volume-title":"Proc.\n                        28th USENIX Conf. Secur. Symp. (SEC)","author":"Carlini","year":"2019"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1145\/3372297.3417270"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1145\/3372297.3417880"},{"key":"ref60","first-page":"5253","article-title":"Extracting training data from\n                        diffusion models","volume-title":"Proc. 32nd USENIX\n                        Secur. Symp. (USENIX Secur.)","author":"Carlini","year":"2023"},{"key":"ref61","first-page":"1345","article-title":"High accuracy and high fidelity extraction of neural\n                        networks","volume-title":"Proc. 29th USENIX Conf. Secur.\n                        Symp. (SEC)","author":"Jagielski","year":"2020"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00474"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.14722\/ndss.2020.24178"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1145\/3052973.3053009"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1145\/3579856.3590336"},{"key":"ref66","article-title":"Data-free adversarial\n                    distillation","author":"Fang","year":"2019"}],"container-title":["IEEE Transactions on Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/ieeexplore.ieee.org\/ielam\/9078688\/11417361\/11099051-aam.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/9078688\/11417361\/11099051.pdf?arnumber=11099051","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,2]],"date-time":"2026-03-02T20:58:59Z","timestamp":1772485139000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11099051\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3]]},"references-count":66,"journal-issue":{"issue":"3"},"URL":"https:\/\/doi.org\/10.1109\/tai.2025.3593470","relation":{},"ISSN":["2691-4581"],"issn-type":[{"value":"2691-4581","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3]]}}}