{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:36:21Z","timestamp":1750221381193,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":16,"publisher":"ACM","license":[{"start":{"date-parts":[[2018,3,29]],"date-time":"2018-03-29T00:00:00Z","timestamp":1522281600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2018,3,29]]},"DOI":"10.1145\/3190645.3190682","type":"proceedings-article","created":{"date-parts":[[2018,3,30]],"date-time":"2018-03-30T12:21:43Z","timestamp":1522412503000},"page":"1-8","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Characterization of differentially private logistic regression"],"prefix":"10.1145","author":[{"given":"Shan","family":"Suthaharan","sequence":"first","affiliation":[{"name":"University of North Carolina at Greensboro"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2018,3,29]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1587\/transinf.2015INP0020"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1587\/transinf.2016INP0019"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1010933404324"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1049\/ip-f-2.1993.0054"},{"key":"e_1_3_2_1_5_1","unstructured":"Kamalika Chaudhuri and Claire Monteleoni. 2009. Privacy-preserving logistic regression. In Advances in Neural Information Processing Systems. 289--296.   Kamalika Chaudhuri and Claire Monteleoni. 2009. Privacy-preserving logistic regression. In Advances in Neural Information Processing Systems. 289--296."},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1007\/11681878_14"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/CSF.2014.35"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-013-5396-x"},{"key":"e_1_3_2_1_9_1","volume-title":"Differentially private distributed logistic regression using private and public data. BMC medical genomics 7, 1","author":"Ji Zhanglong","year":"2014","unstructured":"Zhanglong Ji , Xiaoqian Jiang , Shuang Wang , Li Xiong , and Lucila Ohno-Machado . 2014. Differentially private distributed logistic regression using private and public data. BMC medical genomics 7, 1 ( 2014 ), S14. Zhanglong Ji, Xiaoqian Jiang, Shuang Wang, Li Xiong, and Lucila Ohno-Machado. 2014. Differentially private distributed logistic regression using private and public data. BMC medical genomics 7, 1 (2014), S14."},{"key":"e_1_3_2_1_10_1","volume-title":"Blind source separation based on joint diagonalization in R: The packages JADE and BSSasymp. Journal of Statistical Software 76","author":"Miettinen Jari","year":"2017","unstructured":"Jari Miettinen , Klaus Nordhausen , and Sara Taskinen . 2017. Blind source separation based on joint diagonalization in R: The packages JADE and BSSasymp. Journal of Statistical Software 76 ( 2017 ). Jari Miettinen, Klaus Nordhausen, and Sara Taskinen. 2017. Blind source separation based on joint diagonalization in R: The packages JADE and BSSasymp. Journal of Statistical Software 76 (2017)."},{"key":"e_1_3_2_1_11_1","volume-title":"Differential Privacy: An Estimation Theory-Based Method for Choosing Epsilon. arXiv preprint arXiv:1510.00917","author":"Naldi Maurizio","year":"2015","unstructured":"Maurizio Naldi and Giuseppe D'Acquisto . 2015 . Differential Privacy: An Estimation Theory-Based Method for Choosing Epsilon. arXiv preprint arXiv:1510.00917 (2015). Maurizio Naldi and Giuseppe D'Acquisto. 2015. Differential Privacy: An Estimation Theory-Based Method for Choosing Epsilon. arXiv preprint arXiv:1510.00917 (2015)."},{"key":"e_1_3_2_1_12_1","article-title":"Privacy-Preserving Logistic Regression","volume":"6","author":"Samet Saeed","year":"2015","unstructured":"Saeed Samet . 2015 . Privacy-Preserving Logistic Regression . Journal of Advances in Information Technology Vol 6 , 3 (2015). Saeed Samet. 2015. Privacy-Preserving Logistic Regression. Journal of Advances in Information Technology Vol 6, 3 (2015).","journal-title":"Journal of Advances in Information Technology"},{"key":"e_1_3_2_1_13_1","volume-title":"Machine Learning Models and Algorithms for Big Data Classification: Thinking with Examples for Effective Learning","author":"Suthaharan Shan","year":"2015","unstructured":"Shan Suthaharan . 2015. Machine Learning Models and Algorithms for Big Data Classification: Thinking with Examples for Effective Learning . Springer 36 ( 2015 ). Shan Suthaharan. 2015. Machine Learning Models and Algorithms for Big Data Classification: Thinking with Examples for Effective Learning. Springer 36 (2015)."},{"key":"e_1_3_2_1_14_1","unstructured":"Yyyyyy Xxxxx and Zzzzzzz Xxx. yyyy. It anonymizes this paper to support double-blind reviewing process of the conference. name of the conference (yyyy).  Yyyyyy Xxxxx and Zzzzzzz Xxx. yyyy. It anonymizes this paper to support double-blind reviewing process of the conference. name of the conference (yyyy)."},{"volume-title":"Blind Estimation Using Higher-Order Statistics","author":"Zarzoso V","key":"e_1_3_2_1_15_1","unstructured":"V Zarzoso and AK Nandi . 1999. Blind source separation . In Blind Estimation Using Higher-Order Statistics . Springer , 167--252. V Zarzoso and AK Nandi. 1999. Blind source separation. In Blind Estimation Using Higher-Order Statistics. Springer, 167--252."},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1504\/IJGUC.2013.056250"}],"event":{"name":"ACM SE '18: Southeast Conference","sponsor":["ACM Association for Computing Machinery"],"location":"Richmond Kentucky","acronym":"ACM SE '18"},"container-title":["Proceedings of the ACMSE 2018 Conference"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3190645.3190682","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3190645.3190682","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T02:26:50Z","timestamp":1750213610000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3190645.3190682"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,3,29]]},"references-count":16,"alternative-id":["10.1145\/3190645.3190682","10.1145\/3190645"],"URL":"https:\/\/doi.org\/10.1145\/3190645.3190682","relation":{},"subject":[],"published":{"date-parts":[[2018,3,29]]},"assertion":[{"value":"2018-03-29","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}