{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T21:16:02Z","timestamp":1775855762777,"version":"3.50.1"},"publisher-location":"Cham","reference-count":22,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030646158","type":"print"},{"value":"9783030646165","type":"electronic"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-64616-5_51","type":"book-chapter","created":{"date-parts":[[2020,12,5]],"date-time":"2020-12-05T21:14:46Z","timestamp":1607202886000},"page":"598-610","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Privacy-Preserving Logistic Regression as a Cloud Service Based on Residue Number System"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7209-8324","authenticated-orcid":false,"given":"Jorge M.","family":"Cort\u00e9s-Mendoza","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5029-5212","authenticated-orcid":false,"given":"Andrei","family":"Tchernykh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7066-0061","authenticated-orcid":false,"given":"Mikhail","family":"Babenko","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7384-7670","authenticated-orcid":false,"given":"Luis Bernardo","family":"Pulido-Gayt\u00e1n","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7145-5630","authenticated-orcid":false,"given":"Gleb","family":"Radchenko","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8808-2730","authenticated-orcid":false,"given":"Franck","family":"Leprevost","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8771-8901","authenticated-orcid":false,"given":"Xinheng","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0470-9944","authenticated-orcid":false,"given":"Arutyun","family":"Avetisyan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,12,4]]},"reference":[{"key":"51_CR1","unstructured":"Google. https:\/\/cloud.google.com\/products\/ai. Accessed 13 Mar 2020"},{"key":"51_CR2","unstructured":"Microsoft. https:\/\/azure.microsoft.com\/en-us\/services\/machine-learning. Accessed 13 Mar 2020"},{"key":"51_CR3","unstructured":"Amazon. https:\/\/aws.amazon.com\/machine-learning. Accessed 13 Mar 2020"},{"key":"51_CR4","unstructured":"CSO. https:\/\/www.csoonline.com\/article\/3441477\/enabling-public-but-secure-deep-learning.html. Accessed 13 Mar 2020"},{"key":"51_CR5","unstructured":"PALISADE. https:\/\/palisade-crypto.org\/community. Accessed 13 Mar 2020"},{"key":"51_CR6","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"554","DOI":"10.1007\/978-3-662-44371-2_31","volume-title":"Advances in Cryptology \u2013 CRYPTO 2014","author":"S Halevi","year":"2014","unstructured":"Halevi, S., Shoup, V.: Algorithms in HElib. In: Garay, J.A., Gennaro, R. (eds.) CRYPTO 2014, Part I. LNCS, vol. 8616, pp. 554\u2013571. Springer, Heidelberg (2014). https:\/\/doi.org\/10.1007\/978-3-662-44371-2_31"},{"key":"51_CR7","unstructured":"HEANN. https:\/\/github.com\/snucrypto\/HEAAN. Accessed 13 Mar 2020"},{"key":"51_CR8","doi-asserted-by":"crossref","unstructured":"SEAL. https:\/\/github.com\/Microsoft\/SEAL. Accessed 13 Mar 2020","DOI":"10.1016\/S1350-4789(21)00037-4"},{"key":"51_CR9","doi-asserted-by":"publisher","first-page":"1080","DOI":"10.1016\/j.future.2017.09.061","volume":"92","author":"N Chervyakov","year":"2019","unstructured":"Chervyakov, N., Babenko, M., Tchernykh, A., Kucherov, N., Miranda-L\u00f3pez, V., Cort\u00e9s-Mendoza, J.M.: AR-RRNS: configurable reliable distributed data storage systems for Internet of Things to ensure security. Futur. Gener. Comput. Syst. 92, 1080\u20131092 (2019). https:\/\/doi.org\/10.1016\/j.future.2017.09.061","journal-title":"Futur. Gener. Comput. Syst."},{"key":"51_CR10","doi-asserted-by":"publisher","unstructured":"Aono, Y., Hayashi, T., Trieu Phong, L., Wang, L.: Scalable and secure logistic regression via homomorphic encryption. In: Proceedings of the Sixth ACM on Conference on Data and Application Security and Privacy - CODASPY 2016, pp. 142\u2013144. ACM Press, New York (2016). https:\/\/doi.org\/10.1145\/2857705.2857731","DOI":"10.1145\/2857705.2857731"},{"key":"51_CR11","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1186\/s12920-018-0398-y","volume":"11","author":"C Bonte","year":"2018","unstructured":"Bonte, C., Vercauteren, F.: Privacy-preserving logistic regression training. BMC Med. Genomics 11, 86 (2018). https:\/\/doi.org\/10.1186\/s12920-018-0398-y","journal-title":"BMC Med. Genomics"},{"key":"51_CR12","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1186\/s12920-018-0401-7","volume":"11","author":"A Kim","year":"2018","unstructured":"Kim, A., Song, Y., Kim, M., Lee, K., Cheon, J.H.: Logistic regression model training based on the approximate homomorphic encryption. BMC Med. Genomics 11, 83 (2018)","journal-title":"BMC Med. Genomics"},{"key":"51_CR13","doi-asserted-by":"publisher","unstructured":"Cheon, J.H., Han, K., Kim, A., Kim, M., Song, Y.: A full RNS variant of approximate homomorphic encryption. In: Cid, C., Jacobson, Jr. M. (eds.) Selected Areas in Cryptography \u2013 SAC 2018. LNCS, vol. 11349, pp. 347\u2009\u2212\u2009368. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-10970-7_16","DOI":"10.1007\/978-3-030-10970-7_16"},{"key":"51_CR14","doi-asserted-by":"publisher","first-page":"46938","DOI":"10.1109\/ACCESS.2018.2866697","volume":"6","author":"JH Cheon","year":"2018","unstructured":"Cheon, J.H., Kim, D., Kim, Y., Song, Y.: Ensemble method for privacy-preserving logistic regression based on homomorphic encryption. IEEE Access 6, 46938\u201346948 (2018)","journal-title":"IEEE Access"},{"key":"51_CR15","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1007\/978-3-030-34339-2_2","volume-title":"Information Security Practice and Experience","author":"JS Yoo","year":"2019","unstructured":"Yoo, J.S., Hwang, J.H., Song, B.K., Yoon, J.W.: A bitwise logistic regression using binary approximation and real number division in homomorphic encryption scheme. In: Heng, S.-H., Lopez, J. (eds.) ISPEC 2019. LNCS, vol. 11879, pp. 20\u201340. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-34339-2_2"},{"key":"51_CR16","doi-asserted-by":"publisher","unstructured":"Tchernykh, A., et al.: Towards mitigating uncertainty of data security breaches and collusion in cloud computing. In: 2017 28th International Workshop on Database and Expert Systems Applications (DEXA), pp. 137\u2013141. IEEE (2017). https:\/\/doi.org\/10.1109\/DEXA.2017.44","DOI":"10.1109\/DEXA.2017.44"},{"issue":"4","key":"51_CR17","doi-asserted-by":"publisher","first-page":"1173","DOI":"10.1007\/s10586-018-02896-9","volume":"22","author":"A Tchernykh","year":"2019","unstructured":"Tchernykh, A., et al.: Performance evaluation of secret sharing schemes with data recovery in secured and reliable heterogeneous multi-cloud storage. Cluster Comput. 22(4), 1173\u20131185 (2019). https:\/\/doi.org\/10.1007\/s10586-018-02896-9","journal-title":"Cluster Comput."},{"key":"51_CR18","doi-asserted-by":"crossref","unstructured":"Babenko, M., et al.: Unfairness correction in P2P grids based on residue number system of a special form. In: 2017 28th International Workshop on Database and Expert Systems Applications (DEXA), pp. 147\u2013151. IEEE (2017)","DOI":"10.1109\/DEXA.2017.46"},{"issue":"8","key":"51_CR19","doi-asserted-by":"publisher","first-page":"532","DOI":"10.1134\/S0361768819080115","volume":"45","author":"M Babenko","year":"2019","unstructured":"Babenko, M., et al.: Positional characteristics for efficient number comparison over the homomorphic encryption. Program. Comput. Softw. 45(8), 532\u2013543 (2019). https:\/\/doi.org\/10.1134\/S0361768819080115","journal-title":"Program. Comput. Softw."},{"key":"51_CR20","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1016\/j.ijar.2018.07.010","volume":"102","author":"A Tchernykh","year":"2018","unstructured":"Tchernykh, A., et al.: AC-RRNS: anti-collusion secured data sharing scheme for cloud storage. Int. J. Approx. Reason. 102, 60\u201373 (2018). https:\/\/doi.org\/10.1016\/j.ijar.2018.07.010","journal-title":"Int. J. Approx. Reason."},{"key":"51_CR21","unstructured":"Smith, J.W., Everhart, J.E., Dickson, W.C., Knowler, W.C., Johannes, R.S.: Using the ADAP learning algorithm to forecast the onset of diabetes mellitus. In: Proceedings of the Annual Symposium on Computer Application in Medical Care, p. 261 (1988)"},{"key":"51_CR22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/JIOT.2020.2981276","volume":"7","author":"A Tchernykh","year":"2020","unstructured":"Tchernykh, A., et al.: Scalable data storage design for non-stationary IoT environment with adaptive security and reliability. IEEE Internet Things J. 7, 1 (2020). https:\/\/doi.org\/10.1109\/JIOT.2020.2981276","journal-title":"IEEE Internet Things J."}],"container-title":["Communications in Computer and Information Science","Supercomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-64616-5_51","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,4,24]],"date-time":"2021-04-24T03:41:39Z","timestamp":1619235699000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-64616-5_51"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030646158","9783030646165"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-64616-5_51","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"4 December 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"RuSCDays","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Russian Supercomputing Days","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Moscow","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Russia","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 September 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 September 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ruscdays2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/russianscdays.org\/en","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"106","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"51","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"48% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"The conference was held virtually due to the COVID-19 pandemic.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}