{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T16:19:32Z","timestamp":1776183572368,"version":"3.50.1"},"reference-count":19,"publisher":"IEEE","license":[{"start":{"date-parts":[[2021,7,12]],"date-time":"2021-07-12T00:00:00Z","timestamp":1626048000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,7,12]],"date-time":"2021-07-12T00:00:00Z","timestamp":1626048000000},"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":[[2021,7,12]]},"DOI":"10.1109\/lanman52105.2021.9478813","type":"proceedings-article","created":{"date-parts":[[2021,7,12]],"date-time":"2021-07-12T17:36:18Z","timestamp":1626111378000},"page":"1-6","source":"Crossref","is-referenced-by-count":8,"title":["Model Fragmentation, Shuffle and Aggregation to Mitigate Model Inversion in Federated Learning"],"prefix":"10.1109","author":[{"given":"Hiroki","family":"Masuda","sequence":"first","affiliation":[{"name":"Graduate School of Information Science and Technology, Osaka University,Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kentaro","family":"Kita","sequence":"additional","affiliation":[{"name":"Graduate School of Information Science and Technology, Osaka University,Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuki","family":"Koizumi","sequence":"additional","affiliation":[{"name":"Graduate School of Information Science and Technology, Osaka University,Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junji","family":"Takemasa","sequence":"additional","affiliation":[{"name":"Graduate School of Information Science and Technology, Osaka University,Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Toru","family":"Hasegawa","sequence":"additional","affiliation":[{"name":"Graduate School of Information Science and Technology, Osaka University,Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1145\/2810103.2813677"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1145\/3133956.3134012"},{"key":"ref12","article-title":"Slalom: Fast, verifiable and private execution of neural networks in trusted hardware","author":"tramer","year":"2019","journal-title":"Proceedings of ICLR"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/DSN.2019.00044"},{"key":"ref14","article-title":"Extremal mechanisms for local differential privacy","volume":"17","author":"kairouz","year":"2016","journal-title":"J Mach Learn Res"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1145\/2976749.2978318"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1145\/3133956.3133982"},{"key":"ref17","article-title":"Practical secure aggregation for federated learning on user-held data","author":"bonawitz","year":"2016","journal-title":"Proceedings of PMPML"},{"key":"ref18","article-title":"The mnist database of handwritten digits","author":"he","year":"1998"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM.2019.8737416"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1145\/3298981"},{"key":"ref6","article-title":"Calibrating noise to sensitivity in private data analysis","author":"dwork","year":"2006","journal-title":"Proc Conf Theory of Cryptography"},{"key":"ref5","article-title":"Generative adversarial nets","volume":"27","author":"goodfellow","year":"2014","journal-title":"Adv Neural Inf Process Syst"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2020.2988575"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1561\/0400000042"},{"key":"ref2","article-title":"Federated learning: Strategies for improving communication efficiency","author":"kone?n\u00fd","year":"2016"},{"key":"ref1","article-title":"Federated optimization: Distributed machine learning for on-device intelligence","author":"kone?n\u00fd","year":"2016"},{"key":"ref9","doi-asserted-by":"crossref","DOI":"10.1145\/3338501.3357370","article-title":"A hybrid approach to privacy-preserving federated learning","author":"truex","year":"2019","journal-title":"Proceedings of ACM AISec"}],"event":{"name":"2021 IEEE International Symposium on Local and Metropolitan Area Networks (LANMAN)","location":"Boston, MA, USA","start":{"date-parts":[[2021,7,12]]},"end":{"date-parts":[[2021,7,14]]}},"container-title":["2021 IEEE International Symposium on Local and Metropolitan Area Networks (LANMAN)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9478662\/9478666\/09478813.pdf?arnumber=9478813","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,8,2]],"date-time":"2022-08-02T19:51:43Z","timestamp":1659469903000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9478813\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,12]]},"references-count":19,"URL":"https:\/\/doi.org\/10.1109\/lanman52105.2021.9478813","relation":{},"subject":[],"published":{"date-parts":[[2021,7,12]]}}}