{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T14:17:06Z","timestamp":1784211426171,"version":"3.55.0"},"publisher-location":"New York, NY, USA","reference-count":57,"publisher":"ACM","license":[{"start":{"date-parts":[[2019,11,11]],"date-time":"2019-11-11T00:00:00Z","timestamp":1573430400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"NSERC Discovery Grants"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2019,11,11]]},"DOI":"10.1145\/3338466.3358926","type":"proceedings-article","created":{"date-parts":[[2019,11,11]],"date-time":"2019-11-11T18:15:00Z","timestamp":1573496100000},"page":"57-68","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":83,"title":["PrivFL"],"prefix":"10.1145","author":[{"given":"Kalikinkar","family":"Mandal","sequence":"first","affiliation":[{"name":"University of Waterloo, Waterloo, ON, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guang","family":"Gong","sequence":"additional","affiliation":[{"name":"University of Waterloo, Waterloo, ON, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2019,11,11]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"Ai.type. https:\/\/www.androidauthority.com\/ai-type-data-exposed-820431\/.  Ai.type. https:\/\/www.androidauthority.com\/ai-type-data-exposed-820431\/."},{"key":"e_1_3_2_1_2_1","first-page":"142","volume-title":"Proceedings of the Sixth ACM Conference on Data and Application Security and Privacy(2016)","author":"Aono Y."},{"key":"e_1_3_2_1_3_1","unstructured":"Auto MPG data set.https:\/\/archive.ics.uci.edu\/ml\/datasets\/auto+mpg 1993.Online; accessed 29 July 2019.  Auto MPG data set.https:\/\/archive.ics.uci.edu\/ml\/datasets\/auto+mpg 1993.Online; accessed 29 July 2019."},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1007\/978-3-319-66402-6_10","volume-title":"Computer Security -- ESORICS 2017(Cham","author":"Barbosa M.","year":"2017"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/TDSC.2016.2587623"},{"key":"e_1_3_2_1_6_1","unstructured":"Bonawitz K. Eichner H. Grieskamp W. Huba D. Ingerman A. Ivanov V. Kiddon C. Konecn\u00fd J. Mazzocchi S. McMahan H. B. Overveldt T. V. Petrou D. Ramage D. and Roselander J.Towards federated learning atscale: System design. CoRR abs\/1902.01046(2019).  Bonawitz K. Eichner H. Grieskamp W. Huba D. Ingerman A. Ivanov V. Kiddon C. Konecn\u00fd J. Mazzocchi S. McMahan H. B. Overveldt T. V. Petrou D. Ramage D. and Roselander J.Towards federated learning atscale: System design. CoRR abs\/1902.01046(2019)."},{"key":"e_1_3_2_1_7_1","first-page":"1175","volume-title":"Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security (New York, NY, USA, 2017), CCS '17, ACM","author":"Bonawitz K."},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"crossref","unstructured":"Bonte C. and Vercauteren F. Privacy-preserving logistic regression training. Tech. rep. IACR Cryptology ePrint Archive 233 2018.  Bonte C. and Vercauteren F. Privacy-preserving logistic regression training. Tech. rep. IACR Cryptology ePrint Archive 233 2018.","DOI":"10.1186\/s12920-018-0398-y"},{"key":"e_1_3_2_1_9_1","unstructured":"Boston Housing Dataset.https:\/\/archive.ics.uci.edu\/ml\/machine-learning-databases\/housing\/ 2019. Online; accessed 29 July 2019.  Boston Housing Dataset.https:\/\/archive.ics.uci.edu\/ml\/machine-learning-databases\/housing\/ 2019. Online; accessed 29 July 2019."},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1515\/popets-2017-0045"},{"key":"e_1_3_2_1_11_1","unstructured":"Census Income Data Set.https:\/\/archive.ics.uci.edu\/ml\/datasets\/census+income 1996. Online; accessed 29 July 2019.  Census Income Data Set.https:\/\/archive.ics.uci.edu\/ml\/datasets\/census+income 1996. Online; accessed 29 July 2019."},{"key":"e_1_3_2_1_12_1","first-page":"259","volume-title":"Proceedings of the 14th USENIX Conference on Networked Systems Design and Implementation (Berkeley, CA, USA, 2017), NSDI'17, USENIX Association","author":"Corrigan-Gibbs H."},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.dss.2009.05.016"},{"key":"e_1_3_2_1_14_1","unstructured":"Diabetes Data Set. https:\/\/archive.ics.uci.edu\/ml\/datasets\/diabetes 1994.  Diabetes Data Set. https:\/\/archive.ics.uci.edu\/ml\/datasets\/diabetes 1994."},{"key":"e_1_3_2_1_15_1","first-page":"6","article-title":"New directions in cryptography","volume":"22","author":"Diffie W.","year":"2006","journal-title":"IEEE Trans. Inf. Theor."},{"key":"e_1_3_2_1_16_1","unstructured":"Du W. Han Y. S. and Chen S.Privacy-Preserving Multivariate Statistical Analysis: Linear Regression and Classification. pp. 222--233.  Du W. Han Y. S. and Chen S.Privacy-Preserving Multivariate Statistical Analysis: Linear Regression and Classification. pp. 222--233."},{"key":"e_1_3_2_1_17_1","unstructured":"Dua D. and Graff C. UCI machine learning repository. https:\/\/archive.ics.uci.edu\/ml\/datasets\/Breast+Cancer+Wisconsin+(Diagnostic) and https:\/\/archive.ics.uci.edu\/ml\/datasets\/credit+approval 2017.  Dua D. and Graff C. UCI machine learning repository. https:\/\/archive.ics.uci.edu\/ml\/datasets\/Breast+Cancer+Wisconsin+(Diagnostic) and https:\/\/archive.ics.uci.edu\/ml\/datasets\/credit+approval 2017."},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1561\/0400000042"},{"key":"e_1_3_2_1_19_1","unstructured":"Fanaee-T H. and Gama J. Event labeling combining ensemble detectors and background knowledge. Progress in Artificial Intelligence(2013) 1--15.  Fanaee-T H. and Gama J. Event labeling combining ensemble detectors and background knowledge. Progress in Artificial Intelligence(2013) 1--15."},{"key":"e_1_3_2_1_20_1","first-page":"638","volume-title":"Proceedings of the 18th International Conference on Autonomous Agents and Multi Agent Systems (Richland, SC, 2019), AAMAS '19, International Foundation for Autonomous Agents and Multi agent Systems","author":"Fioretto F."},{"key":"e_1_3_2_1_21_1","first-page":"1322","volume-title":"Proceedings of the 22Nd ACM SIGSAC Conference on Computer and Communications Security (New York, NY, USA, 2015), CCS '15, ACM","author":"Fredrikson M."},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1515\/popets-2017-0053"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1515\/popets-2017-0053"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1145\/1536414.1536440"},{"key":"e_1_3_2_1_25_1","first-page":"104","volume-title":"Heidelberg","author":"Goethals B.","year":"2005"},{"key":"e_1_3_2_1_26_1","first-page":"1","volume-title":"Information Security and Cryptology -- ICISC 2012(Berlin,Heidelberg","author":"Graepel T.","year":"2013"},{"key":"e_1_3_2_1_27_1","unstructured":"Granlund T. etal GMP: the GNU multiple precision arithmetic library 1991.  Granlund T. et al. GMP: the GNU multiple precision arithmetic library 1991."},{"key":"e_1_3_2_1_28_1","first-page":"4","article-title":"Secure multiple linear regression based on homomorphic encryption","volume":"27","author":"Hall R.","year":"2011","journal-title":"Journal of Official Statistics"},{"key":"e_1_3_2_1_29_1","unstructured":"Hardy S. Henecka W. Ivey-Law H. Nock R. Patrini G. Smith G. and Thorne B. Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption. CoRR abs\/1711.10677(2017).  Hardy S. Henecka W. Ivey-Law H. Nock R. Patrini G. Smith G. and Thorne B. Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption. CoRR abs\/1711.10677(2017)."},{"key":"e_1_3_2_1_30_1","unstructured":"Hart W. Johansson F. and Pancratz S. FLINT: Fast Library for Number Theory 2013. Version 2.4.0 http:\/\/flintlib.org.  Hart W. Johansson F. and Pancratz S. FLINT: Fast Library for Number Theory 2013. Version 2.4.0 http:\/\/flintlib.org."},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1007\/978-3-642-38348-9_5","volume-title":"Advances in Cryptology -- EUROCRYPT 2013(Berlin","author":"Joye M.","year":"2013"},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.5555\/1124191.1124299"},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1198\/106186005X47714"},{"key":"e_1_3_2_1_34_1","unstructured":"Karr A. F. Lin X. Sanil A. P. and Reiter J. P. Privacy-preserving analysis of vertically partitioned data using secure matrix products. J. Official Statistics(2009).  Karr A. F. Lin X. Sanil A. P. and Reiter J. P. Privacy-preserving analysis of vertically partitioned data using secure matrix products. J. Official Statistics(2009)."},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-30576-7_16"},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12920-018-0401-7"},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"crossref","unstructured":"Kim M. Song Y. Wang S. Xia Y. and Jiang X.Secure logistic regression based on homomorphic encryption: Design and evaluation. JMIR medical informatics 6 2 (2018).  Kim M. Song Y. Wang S. Xia Y. and Jiang X.Secure logistic regression based on homomorphic encryption: Design and evaluation. JMIR medical informatics 6 2 (2018).","DOI":"10.2196\/medinform.8805"},{"key":"e_1_3_2_1_38_1","first-page":"202","volume-title":"Proceedings of the Second International Conference on Knowledge Discovery and Data Mining(1996)","author":"Kohavi R."},{"key":"e_1_3_2_1_39_1","volume-title":"NIPS Workshop on Private Multi-Party Machine Learning(2016)","author":"Kone-\u0102n\u00c3\u00a1 J."},{"key":"e_1_3_2_1_40_1","first-page":"619","volume-title":"Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security(New York, NY, USA, 2017), CCS '17, ACM","author":"Liu J."},{"key":"e_1_3_2_1_41_1","unstructured":"Liu Y. Chen T. and Yang Q. Secure federated transfer learning. CoRRabs\/1812.03337(2018).  Liu Y. Chen T. and Yang Q. Secure federated transfer learning. CoRRabs\/1812.03337(2018)."},{"key":"e_1_3_2_1_44_1","unstructured":"McMahan H. B. Moore E. Ramage D. Hampson S. etal Communication-efficient learning of deep networks from decentralized data. arXiv preprintar Xiv:1602.05629(2016).  McMahan H. B. Moore E. Ramage D. Hampson S. et al. Communication-efficient learning of deep networks from decentralized data. arXiv preprintar Xiv:1602.05629(2016)."},{"key":"e_1_3_2_1_45_1","unstructured":"McMahan H. B. Moore E. Ramage D. and y Arcas B. A. Federated learning of deep networks using model averaging. CoRR abs\/1602.05629(2016).  McMahan H. B. Moore E. Ramage D. and y Arcas B. A. Federated learning of deep networks using model averaging. CoRR abs\/1602.05629(2016)."},{"key":"e_1_3_2_1_46_1","first-page":"35","volume-title":"Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security(New York, NY, USA, 2018), CCS '18, ACM","author":"Mohassel P."},{"key":"e_1_3_2_1_48_1","first-page":"334","volume-title":"Proceedings of the 2013 IEEE Symposium on Security and Privacy (Washington,DC, USA, 2013), SP '13, IEEE Computer Society","author":"Nikolaenko V."},{"key":"e_1_3_2_1_49_1","unstructured":"Open SSL.The openssl library. https:\/\/www.openssl.org\/.  Open SSL.The openssl library. https:\/\/www.openssl.org\/."},{"key":"e_1_3_2_1_50_1","first-page":"223","volume-title":"Proceedings of the 17th International Conference on Theory and Application of Cryptographic Techniques(Berlin, Heidelberg, 1999), EUROCRYPT'99, Springer-Verlag","author":"Paillier P."},{"key":"e_1_3_2_1_51_1","doi-asserted-by":"publisher","DOI":"10.1145\/1014052.1014139"},{"key":"e_1_3_2_1_52_1","first-page":"1310","volume-title":"Proceedings of the 22Nd ACM SIGSAC Conference on Computer and Communications Security (New York, NY, USA, 2015), CCS '15, ACM","author":"Shokri R."},{"key":"e_1_3_2_1_53_1","first-page":"601","volume-title":"25th USENIX Security Symposium (USENIX Security 16)","author":"Tram\u00e8r F.","year":"2016"},{"key":"e_1_3_2_1_54_1","doi-asserted-by":"crossref","unstructured":"Truex S. Baracaldo N. Anwar A. Steinke T. Ludwig H. and Zhang R. Ahybrid approach to privacy-preserving federated learning. CoRR abs\/1812.03224(2018).  Truex S. Baracaldo N. Anwar A. Steinke T. Ludwig H. and Zhang R. Ahybrid approach to privacy-preserving federated learning. CoRR abs\/1812.03224(2018).","DOI":"10.1145\/3338501.3357370"},{"key":"e_1_3_2_1_55_1","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2009.2036000"},{"key":"e_1_3_2_1_56_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.enbuild.2012.03.003"},{"key":"e_1_3_2_1_57_1","first-page":"102","volume-title":"Seventeenth Annual Computer Security Applications Conference(Dec2001)","author":"Wenliang Du"},{"key":"e_1_3_2_1_58_1","first-page":"162","volume-title":"Proceedings of the 27th Annual Symposium on Foundations of Computer Science (Washington, DC, USA, 1986), SFCS '86, IEEE Computer Society","author":"Yao A. C.-C."},{"key":"e_1_3_2_1_59_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2007.12.020"},{"key":"e_1_3_2_1_60_1","doi-asserted-by":"publisher","DOI":"10.1504\/IJGUC.2013.056250"}],"event":{"name":"CCS '19: 2019 ACM SIGSAC Conference on Computer and Communications Security","location":"London United Kingdom","acronym":"CCS '19","sponsor":["SIGSAC ACM Special Interest Group on Security, Audit, and Control"]},"container-title":["Proceedings of the 2019 ACM SIGSAC Conference on Cloud Computing Security Workshop"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3338466.3358926","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3338466.3358926","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T00:43:21Z","timestamp":1750207401000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3338466.3358926"}},"subtitle":["Practical Privacy-preserving Federated Regressions on High-dimensional Data over Mobile Networks"],"short-title":[],"issued":{"date-parts":[[2019,11,11]]},"references-count":57,"alternative-id":["10.1145\/3338466.3358926","10.1145\/3338466"],"URL":"https:\/\/doi.org\/10.1145\/3338466.3358926","relation":{},"subject":[],"published":{"date-parts":[[2019,11,11]]},"assertion":[{"value":"2019-11-11","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}