{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T05:32:30Z","timestamp":1740115950135,"version":"3.37.3"},"reference-count":38,"publisher":"IEEE","license":[{"start":{"date-parts":[[2024,11,18]],"date-time":"2024-11-18T00:00:00Z","timestamp":1731888000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,11,18]],"date-time":"2024-11-18T00:00:00Z","timestamp":1731888000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,11,18]]},"DOI":"10.1109\/healthcom60970.2024.10880846","type":"proceedings-article","created":{"date-parts":[[2025,2,18]],"date-time":"2025-02-18T18:18:13Z","timestamp":1739902693000},"page":"1-6","source":"Crossref","is-referenced-by-count":0,"title":["Integrity Verifiable Privacy-preserving Federated Learning for Healthcare-IoT"],"prefix":"10.1109","author":[{"given":"Jiarui","family":"Li","sequence":"first","affiliation":[{"name":"Stevens Institute of Technology,Department of Electrical and Computer Engineering,Hoboken,United States"}]},{"given":"Shucheng","family":"Yu","sequence":"additional","affiliation":[{"name":"Stevens Institute of Technology,Department of Electrical and Computer Engineering,Hoboken,United States"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jii.2020.100129"},{"key":"ref2","article-title":"Federated learning: Strategies for improving communication efficiency","author":"Konecny","year":"2016","journal-title":"arXiv preprint"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TCBB.2023.3243932"},{"key":"ref4","first-page":"1605","article-title":"Local model poisoning attacks to {Byzantine-Robust} federated learning","volume-title":"29th USENIX security symposium (USENIX Security 20)","author":"Fang"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/MWC.001.1900119"},{"issue":"1-9","key":"ref6","first-page":"16","article-title":"Exploiting machine learning to subvert your spam filter","volume":"8","author":"Nelson","year":"2008","journal-title":"LEET"},{"key":"ref7","article-title":"Poisoning attacks against support vector machines","author":"Biggio","year":"2012","journal-title":"arXiv preprint"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2021.3128646"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.14778\/3229863.3236266"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/Healthcom56612.2023.10472338"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1145\/3133956.3133982"},{"key":"ref12","first-page":"2938","article-title":"How to backdoor federated learning","volume-title":"International Conference on Artificial Intelligence and Statistics","author":"Bagdasaryan"},{"key":"ref13","first-page":"903","article-title":"Draco: Byzantine-resilient distributed training via redundant gradients","volume-title":"International Conference on Machine Learning","author":"Chen"},{"key":"ref14","article-title":"Verifiable fully homo-morphic encryption","author":"Viand","year":"2023","journal-title":"arXiv preprint"},{"key":"ref15","article-title":"Verifiable encodings for secure homomorphic analytics","author":"Chatel","year":"2022","journal-title":"ar Xiv preprint"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1007\/s00145-023-09481-3"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/SP40001.2021.00106"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/SP46214.2022.9833596"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/EuroSP57164.2023.00052"},{"key":"ref20","article-title":"Privado: Practical and secure dnn inference with enclaves","author":"Grover","year":"2018","journal-title":"ar Xiv preprint"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1145\/3411508.3421376"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW53098.2021.00368"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/RTSS52674.2021.00018"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/CCGrid49817.2020.00-41"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM41043.2020.9155414"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1145\/3133956.3134095"},{"key":"ref27","first-page":"634","article-title":"Analyzing feder-ated learning through an adversarial lens","volume-title":"International Conference on Machine Learning","author":"Bhagoji"},{"key":"ref28","article-title":"A little is enough: Circumventing defenses for distributed learning","author":"Baruch","year":"2019","journal-title":"ar Xiv preprint"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1145\/3446776"},{"key":"ref30","first-page":"27","article-title":"Towards poisoning of deep learning algorithms with back-gradient optimization","volume-title":"Proceedings of the 10th ACM workshop on artificial intelligence and security","author":"Mufioz-Gonzalcz"},{"key":"ref31","article-title":"Poison frogs! targeted clean-label poisoning attacks on neural networks","volume":"31","author":"Shafahi","year":"2018","journal-title":"Advances in neural information processing systems"},{"key":"ref32","article-title":"Targeted backdoor attacks on deep learning systems using data poisoning","author":"Chen","year":"2017","journal-title":"ar Xiv preprint"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58951-6_24"},{"key":"ref34","article-title":"Federated multi-task learning","volume":"30","author":"Smith","year":"2017","journal-title":"Advances in neural information processing systems"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1145\/2046684.2046692"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.14722\/ndss.2021.24434"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.14722\/ndss.2021.24498"},{"journal-title":"Intel xeon scalable platform built for most sensitive workloads","article-title":"Intel","year":"2020","key":"ref38"}],"event":{"name":"2024 IEEE International Conference on E-health Networking, Application &amp; Services (HealthCom)","start":{"date-parts":[[2024,11,18]]},"location":"Nara, Japan","end":{"date-parts":[[2024,11,20]]}},"container-title":["2024 IEEE International Conference on E-health Networking, Application &amp;amp; Services (HealthCom)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/10880688\/10880689\/10880846.pdf?arnumber=10880846","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,20]],"date-time":"2025-02-20T20:01:57Z","timestamp":1740081717000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10880846\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,18]]},"references-count":38,"URL":"https:\/\/doi.org\/10.1109\/healthcom60970.2024.10880846","relation":{},"subject":[],"published":{"date-parts":[[2024,11,18]]}}}