{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T15:56:11Z","timestamp":1774540571707,"version":"3.50.1"},"reference-count":83,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"am","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"US NIH","award":["1R01DA040487"],"award-info":[{"award-number":["1R01DA040487"]}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["CCF-1453432"],"award-info":[{"award-number":["CCF-1453432"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000185","name":"Defense Advanced Research Projects Agency","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000185","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100006503","name":"Space and Naval Warfare Systems Command","doi-asserted-by":"publisher","award":["N66001-15-C-4070"],"award-info":[{"award-number":["N66001-15-C-4070"]}],"id":[{"id":"10.13039\/100006503","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Signal Process."],"published-print":{"date-parts":[[2021]]},"DOI":"10.1109\/tsp.2021.3126546","type":"journal-article","created":{"date-parts":[[2021,11,11]],"date-time":"2021-11-11T20:26:38Z","timestamp":1636662398000},"page":"6355-6370","source":"Crossref","is-referenced-by-count":14,"title":["A Correlated Noise-Assisted Decentralized Differentially Private Estimation Protocol, and its Application to fMRI Source Separation"],"prefix":"10.1109","volume":"69","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2042-5941","authenticated-orcid":false,"given":"Hafiz","family":"Imtiaz","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jafar","family":"Mohammadi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rogers","family":"Silva","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bradley","family":"Baker","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0040-0365","authenticated-orcid":false,"given":"Sergey M.","family":"Plis","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6123-5282","authenticated-orcid":false,"given":"Anand D.","family":"Sarwate","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9058-0747","authenticated-orcid":false,"given":"Vince D.","family":"Calhoun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref73","doi-asserted-by":"crossref","first-page":"987","DOI":"10.2307\/1912773","article-title":"Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation","volume":"50","year":"1982","journal-title":"Econometrica"},{"key":"ref72","first-page":"2652","article-title":"A stability-based Validation procedure for differentially private machine learning","author":"chaudhuri","year":"2013","journal-title":"Adv Neural Inf Process Syst"},{"key":"ref71","first-page":"9","year":"1998","journal-title":"On-Line Learning in Neural Networks"},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2017.132"},{"key":"ref76","doi-asserted-by":"crossref","first-page":"612","DOI":"10.1145\/359168.359176","article-title":"How to share a secret","volume":"22","year":"1979","journal-title":"Commun ACM"},{"key":"ref77","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.1976.1055638"},{"key":"ref74","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1016\/0304-4076(86)90063-1","article-title":"Generalized autoregressive conditional heteroskedasticity","volume":"31","year":"1986","journal-title":"J Econometrics"},{"key":"ref39","article-title":"Advances and open problems in federated learning","author":"kairouz","year":"2019"},{"key":"ref75","doi-asserted-by":"publisher","DOI":"10.1109\/JSTSP.2015.2425831"},{"key":"ref38","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","volume":"54","author":"mcmahan","year":"0","journal-title":"Proc 20th Int Conf Artif Intell Statist Ser Proc Mach Learn Res"},{"key":"ref78","doi-asserted-by":"publisher","DOI":"10.1109\/JSAIT.2021.3053220"},{"key":"ref79","first-page":"650","article-title":"On significance of the least significant bits for differential privacy","year":"2012","journal-title":"Proc ACM Conf Comput Commun Secur"},{"key":"ref33","first-page":"933","article-title":"A differentially private stochastic gradient descent algorithm for multiparty classification","author":"rajkumar","year":"2012","journal-title":"Artificial Intelligence and Statistics"},{"key":"ref32","first-page":"2719","article-title":"Differentially private empirical risk minimization revisited: Faster and more general","author":"wang","year":"2017","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref31","first-page":"2563","article-title":"Accuracy first: Selecting a differential privacy level for accuracy constrained ERM","author":"ligett","year":"2017","journal-title":"Adv Neural Inf Process Syst"},{"key":"ref30","first-page":"464","article-title":"Private empirical risk minimization: Efficient algorithms and tight error bounds","author":"smith","year":"2014","journal-title":"Proc IEEE 55th Annu Symp Found Comput Sci"},{"key":"ref37","first-page":"94","article-title":"Mechanism design via differential privacy","year":"2007","journal-title":"Proc 48th Annu IEEE Symp Found Comput Sci"},{"key":"ref36","first-page":"813","article-title":"Privacy-preserving statistical estimation with optimal convergence rates","year":"2011","journal-title":"Proc 43rd Annu ACM Symp Theory Comput"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1145\/3035918.3064047"},{"key":"ref34","first-page":"2288","article-title":"Practical locally private heavy hitters","year":"2017","journal-title":"Adv Neural Inf Process Syst"},{"key":"ref60","doi-asserted-by":"crossref","first-page":"4948","DOI":"10.1109\/TIT.2021.3091604","article-title":"Coordination through shared randomness","volume":"67","year":"2021","journal-title":"IEEE Trans Inf Theory"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1109\/CSNT.2013.25"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9781139020411"},{"key":"ref63","first-page":"288","article-title":"SoK: Differential privacies","volume":"2020","year":"0","journal-title":"Proc Privacy Enhancing Technol"},{"key":"ref28","article-title":"Differential privacy and machine learning: A survey and review","author":"ji","year":"2014"},{"key":"ref64","article-title":"Reviewing and improving the gaussian mechanism for differential privacy","author":"zhao","year":"0"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/GlobalSIP.2013.6736861"},{"key":"ref65","first-page":"486","article-title":"On the performance indices of ICA and blind source separation","year":"2011","journal-title":"Proc 12th IEEE Int Workshop Signal Process Adv Wireless Commun"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2018.2794384"},{"key":"ref66","year":"1979","journal-title":"Inequalities Theory of Majorization and Its Applications"},{"key":"ref67","year":"1976","journal-title":"Principles of Mathematical Analysis McGraw-Hill Higher Education"},{"key":"ref68","first-page":"2206","article-title":"Differentially private distributed principal component analysis","year":"2018","journal-title":"Proc IEEE Int Conf Acoust Speech Signal Process"},{"key":"ref69","first-page":"757","article-title":"A new learning algorithm for blind signal separation","year":"1995","journal-title":"Proc 8th Int Conf Neural Inf Process Syst"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2015.11.057"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/CISS.2016.7460488"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/TSG.2017.2720471"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/TAC.2016.2541298"},{"key":"ref21","article-title":"Optimal algorithms for distributed optimization","author":"uribe","year":"2017"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/TSIPN.2018.2797806"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/ACC.2016.7525222"},{"key":"ref26","first-page":"1069","article-title":"Differentially private empirical risk minimization","volume":"12","author":"monteleoni","year":"2011","journal-title":"J Mach Learn Res"},{"key":"ref25","first-page":"289","article-title":"Privacy-preserving logistic regression","volume":"21","author":"chaudhuri","year":"2008","journal-title":"Proc 21st Int Conf Neural Inf Process Syst"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/PST.2015.7232954"},{"key":"ref51","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.neuroimage.2009.01.026","article-title":"An ICA-based method for the identification of optimal fMRI features and components using combined group-discriminative techniques","volume":"46","year":"2009","journal-title":"NeuroImage"},{"key":"ref59","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1561\/0400000042","article-title":"The algorithmic foundations of differential privacy","volume":"9","year":"2013","journal-title":"Found Trends Theor Comput Sci"},{"key":"ref58","first-page":"1","article-title":"Differential privacy","author":"dwork","year":"0","journal-title":"Automata Language and Programming"},{"key":"ref57","doi-asserted-by":"crossref","first-page":"1129","DOI":"10.1162\/neco.1995.7.6.1129","article-title":"An information-maximization approach to blind separation and blind deconvolution","volume":"7","year":"1995","journal-title":"Neural Computation"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1002\/hbm.20166"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.3389\/fnsys.2011.00002"},{"key":"ref54","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1002\/hbm.1048","article-title":"A method for making group inferences from functional MRI data using independent component analysis","volume":"14","year":"2001","journal-title":"Hum Brain Mapping"},{"key":"ref53","article-title":"Temporal and spatial independent component analysis for fMRI data sets embedded in a r. package","author":"bordier","year":"2010"},{"key":"ref52","first-page":"1028","article-title":"Parallel independent component analysis for multimodal analysis: Application to fMRI and EEG data","year":"2007","journal-title":"Proc 4th IEEE Int Symp Biomed Imaging From Nano to Macro"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0073309"},{"key":"ref11","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1109\/RBME.2012.2211076","article-title":"Multisubject independent component analysis of fMRI: A decade of intrinsic networks, default mode, and neurodiagnostic discovery","volume":"5","year":"2012","journal-title":"IEEE Rev Biomed Eng"},{"key":"ref40","first-page":"3229","article-title":"Differentially private bayesian learning on distributed data","author":"heikkil\u00e4","year":"2017","journal-title":"Adv Neural Inf Process Syst"},{"key":"ref12","volume":"36","year":"1994","journal-title":"Signal Process"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2018.10.072"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2008.4497436"},{"key":"ref15","article-title":"Approximate and probabilistic differential privacy definitions","volume":"2018","author":"meiser","year":"2018","journal-title":"IACR Cryptol ePrint Arch"},{"key":"ref82","article-title":"Distributed differentially private computation of functions with correlated noise","year":"2019"},{"key":"ref16","first-page":"263","article-title":"Rényi differential privacy","year":"2017","journal-title":"Proc 30th Comput Secur Found Symp (CSF)"},{"key":"ref81","doi-asserted-by":"publisher","DOI":"10.1109\/FOCS.2010.12"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1145\/2976749.2978318"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/JSTSP.2018.2877842"},{"key":"ref83","first-page":"894","article-title":"Learning from data with heterogeneous noise using SGD","volume":"38","year":"2015","journal-title":"Proc 18th Int Conf Artif Intell Statist"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1561\/2200000016"},{"key":"ref80","doi-asserted-by":"publisher","DOI":"10.29012\/jpc.679"},{"key":"ref4","doi-asserted-by":"crossref","first-page":"408","DOI":"10.1093\/ije\/dyv193","article-title":"ViPAR: A software platform for the virtual pooling and analysis of research data","volume":"45","author":"carter","year":"2015","journal-title":"Int J Epidemiol"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2016.00365"},{"key":"ref6","article-title":"How to break anonymity of the Netflix prize dataset","year":"2006"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1093\/ije\/dyu188"},{"key":"ref8","first-page":"1618","article-title":"Differentially private Kalman filtering","author":"ny","year":"2012","journal-title":"Proc 50th Annu Allerton Conf Commun Control Comput"},{"key":"ref7","first-page":"29","article-title":"Only you, your doctor, and many others may know","volume":"2015092903","year":"2015","journal-title":"SC Technology"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1007\/11761679_29"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1007\/11681878_14"},{"key":"ref46","first-page":"6277","article-title":"Privacy amplification by subsampling: Tight analyses via couplings and divergences","author":"balle","year":"2018","journal-title":"Adv Neural Inf Process Syst"},{"key":"ref45","first-page":"97","article-title":"Characterizing the sample complexity of private learners","year":"2013","journal-title":"Proc 4th Conf Innov Theor Comput Sci"},{"key":"ref48","article-title":"Encode, shuffle, analyze privacy revisited: Formalizations and empirical evaluation","author":"erlingsson","year":"2020"},{"key":"ref47","article-title":"The privacy blanket of the shuffle model","volume":"11693","author":"b","year":"2019","journal-title":"Adv Cryptology - CRYPTO 2019 CRYPTO 2019 ser Lecture Notes Comput Sci"},{"key":"ref42","first-page":"2008","article-title":"Secure multi-party differential privacy","author":"kairouz","year":"2015","journal-title":"Adv Neural Inf Process Syst"},{"key":"ref41","first-page":"155","article-title":"Secure multiparty aggregation with differential privacy: A comparative study","year":"2013","journal-title":"Proc Joint EDBT\/ICDT Workshops"},{"key":"ref44","first-page":"531","article-title":"What can we learn privately","year":"2008","journal-title":"Proc IEEE 49th Annu IEEE Symp Found Comput Sci"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1145\/3133956.3133982"}],"container-title":["IEEE Transactions on Signal Processing"],"original-title":[],"link":[{"URL":"https:\/\/ieeexplore.ieee.org\/ielam\/78\/9307529\/9612077-aam.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/78\/9307529\/09612077.pdf?arnumber=9612077","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T14:50:32Z","timestamp":1652194232000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9612077\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"references-count":83,"URL":"https:\/\/doi.org\/10.1109\/tsp.2021.3126546","relation":{},"ISSN":["1053-587X","1941-0476"],"issn-type":[{"value":"1053-587X","type":"print"},{"value":"1941-0476","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]}}}