{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T14:45:59Z","timestamp":1768315559981,"version":"3.49.0"},"reference-count":24,"publisher":"IEEE","license":[{"start":{"date-parts":[[2025,11,18]],"date-time":"2025-11-18T00:00:00Z","timestamp":1763424000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,11,18]],"date-time":"2025-11-18T00:00:00Z","timestamp":1763424000000},"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":[[2025,11,18]]},"DOI":"10.1109\/icspis67605.2025.11318386","type":"proceedings-article","created":{"date-parts":[[2026,1,12]],"date-time":"2026-01-12T18:20:02Z","timestamp":1768242002000},"page":"1-6","source":"Crossref","is-referenced-by-count":0,"title":["A Cutting-Edge Hash-Comb Approach for Privacy-Preserving Federated Learning"],"prefix":"10.1109","author":[{"given":"Alanoud","family":"Almemari","sequence":"first","affiliation":[{"name":"Khalifa University,EBTIC,Department of Computer Science,Abu Dhabi,UAE"}]},{"given":"Ernesto","family":"Damiani","sequence":"additional","affiliation":[{"name":"Universit&#x2018; a Degli Studi di Milano,Computer Science Department,Abu Dhabi,UAE"}]},{"given":"Yousof","family":"Alhammadi","sequence":"additional","affiliation":[{"name":"Khalifa University,Computer Science Department,Abu Dhabi,UAE"}]},{"given":"Rasool","family":"Asal","sequence":"additional","affiliation":[{"name":"University of Dubai,College of Engineering and IT,Dubai,UAE"}]},{"given":"Maurizio","family":"Colombo","sequence":"additional","affiliation":[{"name":"Khalifa University,EBTIC,Abu Dhabi,UAE"}]},{"given":"Lamees M.","family":"AlQassem","sequence":"additional","affiliation":[{"name":"University of Dubai,College of Engineering and IT,Dubai,UAE"}]}],"member":"263","reference":[{"key":"ref1","article-title":"Federated learning: Opportunities and challenges","author":"Mammen","year":"2021"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1155\/2021\/4376418"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.3390\/electronics11040670"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/MNET.001.2300140"},{"key":"ref5","first-page":"2985","article-title":"Boosting gradient leakage attacks: Data reconstruction in realistic fl settings","volume-title":"Proceedings of the 34th USENIX Security Symposium (SEC \u201925)","author":"Fan"},{"key":"ref6","first-page":"107630","article-title":"A blockchain-based federated learning mechanism for privacy preservation of healthcare iot data","volume":"2023","author":"Zheng","year":"2025","journal-title":"Computers in Biology and Medicine"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/BigData.2018.8622598"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/Blockchain.2019.00057"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/MNET.011.2000263"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2020.3044223"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TCOMM.2020.2990686"},{"key":"ref12","article-title":"Mobile edge computing, blockchain and reputation-based crowdsourcing iot federated learning: A secure, decentralized and privacy-preserving system","author":"Zhao","year":"2019"},{"key":"ref13","article-title":"Blockflow: An accountable and privacy-preserving solution for federated learning","volume-title":"ArXiv","volume":"abs\/2007.03856","author":"Mugunthan","year":"2020"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3021253"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.3390\/electronics9050773"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1145\/3422337.3447837"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2024.127663"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2024.3523909"},{"key":"ref19","first-page":"165","article-title":"A survey of differential privacy techniques for federated learning","volume-title":"Proceedings of the 3rd Asia Conference on Algorithms, Computing and Machine Learning (CACML 2024)","author":"Zheng"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2023.3302065"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1016\/j.iot.2025.101579"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/ICCC52777.2021.9580315"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2022.3158934"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/WiMob55322.2022.9941515"}],"event":{"name":"2025 8th International Conference on Signal Processing and Information Security (ICSPIS)","location":"Dubai, United Arab Emirates","start":{"date-parts":[[2025,11,18]]},"end":{"date-parts":[[2025,11,20]]}},"container-title":["2025 8th International Conference on Signal Processing and Information Security (ICSPIS)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/11318309\/11318310\/11318386.pdf?arnumber=11318386","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T09:16:37Z","timestamp":1768295797000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11318386\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,18]]},"references-count":24,"URL":"https:\/\/doi.org\/10.1109\/icspis67605.2025.11318386","relation":{},"subject":[],"published":{"date-parts":[[2025,11,18]]}}}