{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,10]],"date-time":"2025-12-10T08:55:29Z","timestamp":1765356929111,"version":"3.37.3"},"reference-count":22,"publisher":"IEEE","license":[{"start":{"date-parts":[[2021,6,6]],"date-time":"2021-06-06T00:00:00Z","timestamp":1622937600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2021,6,6]],"date-time":"2021-06-06T00:00:00Z","timestamp":1622937600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,6,6]],"date-time":"2021-06-06T00:00:00Z","timestamp":1622937600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100000781","name":"European Research Council","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000781","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,6,6]]},"DOI":"10.1109\/icassp39728.2021.9413624","type":"proceedings-article","created":{"date-parts":[[2021,5,13]],"date-time":"2021-05-13T19:53:45Z","timestamp":1620935625000},"page":"5195-5199","source":"Crossref","is-referenced-by-count":27,"title":["Private Wireless Federated Learning with Anonymous Over-the-Air Computation"],"prefix":"10.1109","author":[{"given":"Burak","family":"Hasircioglu","sequence":"first","affiliation":[]},{"given":"Deniz","family":"Gunduz","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"year":"2017","author":"mcmahan","article-title":"Learning differentially private recurrent language models","key":"ref10"},{"year":"2017","author":"geyer","article-title":"Differentially private federated learning: A client level perspective","key":"ref11"},{"doi-asserted-by":"publisher","key":"ref12","DOI":"10.1109\/TSP.2020.2981904"},{"doi-asserted-by":"publisher","key":"ref13","DOI":"10.1109\/TWC.2020.2974748"},{"doi-asserted-by":"publisher","key":"ref14","DOI":"10.1109\/TWC.2019.2946245"},{"year":"2020","author":"sonee","article-title":"Efficient federated learning over multiple access channel with differential privacy constraints","key":"ref15"},{"doi-asserted-by":"publisher","key":"ref16","DOI":"10.1109\/ISIT44484.2020.9174426"},{"key":"ref17","doi-asserted-by":"crossref","DOI":"10.1109\/GLOBECOM42002.2020.9322199","article-title":"Differentially private aircomp federated learning with power adaptation harnessing receiver noise","author":"koda","year":"2020"},{"key":"ref18","doi-asserted-by":"crossref","DOI":"10.1109\/JSAC.2020.3036948","article-title":"Privacy for free: Wireless federated learning via uncoded transmission with adaptive power control","author":"liu","year":"2020"},{"doi-asserted-by":"publisher","key":"ref19","DOI":"10.1109\/CSF.2017.11"},{"year":"2020","author":"geiping","article-title":"Inverting gradients&#x2013;how easy is it to break privacy in federated learning?","key":"ref4"},{"key":"ref3","first-page":"14774","article-title":"Deep leakage from gradients","author":"zhu","year":"2019","journal-title":"Advances in neural information processing systems"},{"doi-asserted-by":"publisher","key":"ref6","DOI":"10.1145\/2660267.2660348"},{"doi-asserted-by":"publisher","key":"ref5","DOI":"10.1561\/0400000042"},{"doi-asserted-by":"publisher","key":"ref8","DOI":"10.1109\/TPDS.2019.2899097"},{"doi-asserted-by":"publisher","key":"ref7","DOI":"10.1109\/ICDE.2019.00063"},{"doi-asserted-by":"publisher","key":"ref2","DOI":"10.1109\/SP.2019.00029"},{"key":"ref1","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","author":"mcmahan","year":"2017","journal-title":"Artificial Intelligence and Statistics"},{"doi-asserted-by":"publisher","key":"ref9","DOI":"10.1145\/2976749.2978318"},{"key":"ref20","first-page":"6277","article-title":"Privacy amplification by subsampling: Tight analyses via couplings and divergences","author":"balle","year":"2018","journal-title":"Advances in neural information processing systems"},{"year":"2019","author":"mironov","article-title":"R&#x00E9;nyi differential privacy of the sampled gaussian mechanism","key":"ref22"},{"key":"ref21","first-page":"1226","article-title":"Subsampled r&#x00E9;nyi differential privacy and analytical moments accountant","author":"wang","year":"0"}],"event":{"name":"ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","start":{"date-parts":[[2021,6,6]]},"location":"Toronto, ON, Canada","end":{"date-parts":[[2021,6,11]]}},"container-title":["ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9413349\/9413350\/09413624.pdf?arnumber=9413624","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,27]],"date-time":"2022-12-27T08:27:27Z","timestamp":1672129647000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9413624\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6,6]]},"references-count":22,"URL":"https:\/\/doi.org\/10.1109\/icassp39728.2021.9413624","relation":{},"subject":[],"published":{"date-parts":[[2021,6,6]]}}}