{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T09:08:30Z","timestamp":1781168910633,"version":"3.54.1"},"reference-count":39,"publisher":"IEEE","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018,12]]},"DOI":"10.1109\/bigdata.2018.8622627","type":"proceedings-article","created":{"date-parts":[[2019,1,24]],"date-time":"2019-01-24T22:07:18Z","timestamp":1548367638000},"page":"2413-2422","source":"Crossref","is-referenced-by-count":29,"title":["Privacy-Preserving Scoring of Tree Ensembles: A Novel Framework for AI in Healthcare"],"prefix":"10.1109","author":[{"given":"Kyle","family":"Fritchman","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Keerthanaa","family":"Saminathan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rafael","family":"Dowsley","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tyler","family":"Hughes","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Martine","family":"De Cock","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anderson","family":"Nascimento","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ankur","family":"Teredesai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref39","article-title":"Machine learning models for surgical site infection prediction","author":"mandagani","year":"2016","journal-title":"AMIA KDDM-WG Symposium (American medical Informatics Association Knowledge Discovery and Data Mining Working Group)"},{"key":"ref38","article-title":"Design of large scale applications of secure multiparty computation: Secure linear programming","author":"de hoogh","year":"2012","journal-title":"Ph D Dissertation"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1587\/transfun.E94.A.725"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1007\/s10207-014-0247-8"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2015.2476783"},{"key":"ref30","first-page":"600","article-title":"On the power of correlated randomness in secure computation","volume":"7785","author":"ishai","year":"2013","journal-title":"TCC 2013"},{"key":"ref37","first-page":"35","article-title":"Secure computation with fixed-point numbers","volume":"6052","author":"catrina","year":"2010","journal-title":"FC 2010"},{"key":"ref36","article-title":"Cryptography based on correlated data: Foundations and practice","author":"dowsley","year":"2016","journal-title":"Ph D Dissertation"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-71677-8_22"},{"key":"ref34","first-page":"420","article-title":"Efficient multiparty protocols using circuit ran-domization","author":"beaver","year":"1992","journal-title":"CRYPTO’91"},{"key":"ref10","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/3-540-45014-9_1","article-title":"Ensemble methods in machine learning","volume":"1857","author":"dietterich","year":"2000","journal-title":"Int'l Workshop on Multiple Classifier Systems"},{"key":"ref11","article-title":"The state of artificial intelligence","author":"ng","year":"2017","journal-title":"MIT Tech Rev"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-70992-5_2"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2013.30"},{"key":"ref14","first-page":"179","article-title":"Practical secure decision tree learning in a teletreatment application","volume":"8437","author":"de hoogh","year":"2014","journal-title":"FC 2014 ser LNCS"},{"key":"ref15","article-title":"Fast, privacy preserving linear regression over distributed datasets based on pre-distributed data","author":"de cock","year":"2015","journal-title":"AISec 2015"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1145\/772862.772867"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.14722\/ndss.2015.23241"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1515\/popets-2016-0043"},{"key":"ref19","first-page":"354","article-title":"Efficient unconditionally secure comparison and privacy preserving machine learning classification protocols","volume":"9451","author":"david","year":"2015","journal-title":"ProvSec 2015"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1145\/258533.258637"},{"key":"ref4","article-title":"Google Deepmind data agreement with NHS UK","year":"2016"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/SFCS.2001.959888"},{"key":"ref3","article-title":"Google AI given access to health records of 1.6 million english patients","author":"fiveash","year":"2016","journal-title":"Arstechnica"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1145\/2783258.2788613"},{"key":"ref29","first-page":"337","article-title":"A two-party protocol with trusted initializer for computing the inner product","volume":"6513","author":"dowsley","year":"2011","journal-title":"WISA '10"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1145\/2783258.2788585"},{"key":"ref8","article-title":"Wyden pushes for stronger security in collection of personal information","author":"wyden","year":"2017"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9781107337756"},{"key":"ref2","article-title":"The promise of evidence-based policymaking","year":"2017"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-03549-4_20"},{"key":"ref1","article-title":"Privacy in information-rich intelligent infrastructure","author":"dwork","year":"2017"},{"key":"ref20","article-title":"Efficient and private scoring of decision trees, support vector machines and logistic regression models based on pre-computation","volume":"pp","author":"de cock","year":"2017","journal-title":"IEEE Transactions on Dependable and Secure Computing"},{"key":"ref22","article-title":"Privacy-preserving tax fraud detection in the cloud with realistic data volumes","author":"bogdanov","year":"2016","journal-title":"T-4-24 Cybernetica AS"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-79228-4_1"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1145\/2810103.2813677"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1515\/popets-2016-0019"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1504\/IJSN.2015.071829"},{"key":"ref25","first-page":"601","article-title":"Stealing machine learning models via prediction APIs","author":"tram\u00e8r","year":"2016","journal-title":"USENIX Security Symposium"}],"event":{"name":"2018 IEEE International Conference on Big Data (Big Data)","location":"Seattle, WA, USA","start":{"date-parts":[[2018,12,10]]},"end":{"date-parts":[[2018,12,13]]}},"container-title":["2018 IEEE International Conference on Big Data (Big Data)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/8610059\/8621858\/08622627.pdf?arnumber=8622627","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,26]],"date-time":"2022-01-26T18:12:42Z","timestamp":1643220762000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8622627\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,12]]},"references-count":39,"URL":"https:\/\/doi.org\/10.1109\/bigdata.2018.8622627","relation":{},"subject":[],"published":{"date-parts":[[2018,12]]}}}