{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T22:01:06Z","timestamp":1776290466309,"version":"3.50.1"},"publisher-location":"Cham","reference-count":29,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030299583","type":"print"},{"value":"9783030299590","type":"electronic"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1007\/978-3-030-29959-0_2","type":"book-chapter","created":{"date-parts":[[2019,9,14]],"date-time":"2019-09-14T23:04:10Z","timestamp":1568502250000},"page":"22-40","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":36,"title":["Towards Secure and Efficient Outsourcing of Machine Learning Classification"],"prefix":"10.1007","author":[{"given":"Yifeng","family":"Zheng","sequence":"first","affiliation":[]},{"given":"Huayi","family":"Duan","sequence":"additional","affiliation":[]},{"given":"Cong","family":"Wang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,9,15]]},"reference":[{"issue":"7\u20138","key":"2_CR1","doi-asserted-by":"publisher","first-page":"2387","DOI":"10.1007\/s00521-012-1196-7","volume":"23","author":"AT Azar","year":"2013","unstructured":"Azar, A.T., El-Metwally, S.M.: Decision tree classifiers for automated medical diagnosis. Neural Comput. Appl. 23(7\u20138), 2387\u20132403 (2013)","journal-title":"Neural Comput. Appl."},{"key":"2_CR2","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1007\/978-3-319-66402-6_8","volume-title":"Computer Security \u2013 ESORICS 2017","author":"F Baldimtsi","year":"2017","unstructured":"Baldimtsi, F., Papadopoulos, D., Papadopoulos, S., Scafuro, A., Triandopoulos, N.: Server-aided secure computation with off-line parties. In: Foley, S.N., Gollmann, D., Snekkenes, E. (eds.) ESORICS 2017. LNCS, vol. 10492, pp. 103\u2013123. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-66402-6_8"},{"key":"2_CR3","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"420","DOI":"10.1007\/3-540-46766-1_34","volume-title":"Advances in Cryptology \u2014 CRYPTO 1991","author":"D Beaver","year":"1992","unstructured":"Beaver, D.: Efficient multiparty protocols using circuit randomization. In: Feigenbaum, J. (ed.) CRYPTO 1991. LNCS, vol. 576, pp. 420\u2013432. Springer, Heidelberg (1992). https:\/\/doi.org\/10.1007\/3-540-46766-1_34"},{"key":"2_CR4","doi-asserted-by":"crossref","unstructured":"Bost, R., Popa, R.A., Tu, S., Goldwasser, S.: Machine learning classification over encrypted data. In: Proceedings of NDSS (2015)","DOI":"10.14722\/ndss.2015.23241"},{"key":"2_CR5","doi-asserted-by":"crossref","unstructured":"Brakerski, Z., Gentry, C., Vaikuntanathan, V.: (Leveled) fully homomorphic encryption without bootstrapping. In: Proceediongs of ITCS (2012)","DOI":"10.1145\/2090236.2090262"},{"key":"2_CR6","doi-asserted-by":"crossref","unstructured":"Cai, C., Zheng, Y., Wang, C.: Leveraging crowdsensed data streams to discover and sell knowledge: a secure and efficient realization. In: Proceedings of IEEE ICDCS (2018)","DOI":"10.1109\/ICDCS.2018.00064"},{"issue":"2","key":"2_CR7","doi-asserted-by":"publisher","first-page":"217","DOI":"10.1109\/TDSC.2017.2679189","volume":"16","author":"MD Cock","year":"2017","unstructured":"Cock, M.D., et al.: Efficient and private scoring of decision trees, support vector machines and logistic regression models based on pre-computation. IEEE Trans. Dependable Secure Comput. 16(2), 217\u2013230 (2017). 101109\/TDSC20172679189","journal-title":"IEEE Trans. Dependable Secure Comput."},{"issue":"3","key":"2_CR8","doi-asserted-by":"publisher","first-page":"1053","DOI":"10.1109\/TIFS.2012.2190726","volume":"7","author":"Z Erkin","year":"2012","unstructured":"Erkin, Z., Veugen, T., Toft, T., Lagendijk, R.L.: Generating private recommendations efficiently using homomorphic encryption and data packing. IEEE Trans. Inf. Forensics Secur. 7(3), 1053\u20131066 (2012)","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"2_CR9","doi-asserted-by":"crossref","unstructured":"Goldreich, O., Micali, S., Wigderson, A.: How to play any mental game or A completeness theorem for protocols with honest majority. In: Proceedings of ACM STOC (1987)","DOI":"10.1145\/28395.28420"},{"key":"2_CR10","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"243","DOI":"10.1007\/978-3-319-95729-6_16","volume-title":"Data and Applications Security and Privacy XXXII","author":"M Joye","year":"2018","unstructured":"Joye, M., Salehi, F.: Private yet efficient decision tree evaluation. In: Kerschbaum, F., Paraboschi, S. (eds.) DBSec 2018. LNCS, vol. 10980, pp. 243\u2013259. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-95729-6_16"},{"key":"2_CR11","unstructured":"Juvekar, C., Vaikuntanathan, V., Chandrakasan, A.: GAZELLE: A low latency framework for secure neural network inference. In: Proceedings of USENIX Security Symposium (2018)"},{"issue":"2","key":"2_CR12","doi-asserted-by":"crossref","first-page":"187","DOI":"10.2478\/popets-2019-0026","volume":"2019","author":"\u00c1 Kiss","year":"2019","unstructured":"Kiss, \u00c1., Naderpour, M., Liu, J., Asokan, N., Schneider, T.: Sok: modular and efficient private decision tree evaluation. PoPETs 2019(2), 187\u2013208 (2019)","journal-title":"PoPETs"},{"issue":"6","key":"2_CR13","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1038\/nrg3920","volume":"16","author":"MW Libbrecht","year":"2015","unstructured":"Libbrecht, M.W., Noble, W.S.: Machine learning applications in genetics and genomics. Nat. Rev. Genet. 16(6), 321\u2013332 (2015)","journal-title":"Nat. Rev. Genet."},{"key":"2_CR14","doi-asserted-by":"crossref","unstructured":"Liu, J., Juuti, M., Lu, Y., Asokan, N.: Oblivious neural network predictions via minionn transformations. In: Proceedings of ACM CCS (2017)","DOI":"10.1145\/3133956.3134056"},{"issue":"4","key":"2_CR15","doi-asserted-by":"publisher","first-page":"603","DOI":"10.1016\/j.eswa.2004.12.008","volume":"28","author":"JH Min","year":"2005","unstructured":"Min, J.H., Lee, Y.: Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters. Expert Syst. Appl. 28(4), 603\u2013614 (2005)","journal-title":"Expert Syst. Appl."},{"key":"2_CR16","doi-asserted-by":"crossref","unstructured":"Mohassel, P., Zhang, Y.: Secureml: a system for scalable privacy-preserving machine learning. In: Proceedings of IEEE S&P (2017)","DOI":"10.1109\/SP.2017.12"},{"key":"2_CR17","doi-asserted-by":"crossref","unstructured":"Nikolaenko, V., Ioannidis, S., Weinsberg, U., Joye, M., Taft, N., Boneh, D.: Privacy-preserving matrix factorization. In: Proceedings of ACM CCS (2013)","DOI":"10.1145\/2508859.2516751"},{"key":"2_CR18","doi-asserted-by":"crossref","unstructured":"Nikolaenko, V., Weinsberg, U., Ioannidis, S., Joye, M., Boneh, D., Taft, N.: Privacy-preserving ridge regression on hundreds of millions of records. In: Proceedings of IEEE SP (2013)","DOI":"10.1109\/SP.2013.30"},{"key":"2_CR19","doi-asserted-by":"crossref","unstructured":"Riazi, M.S., Weinert, C., Tkachenko, O., Songhori, E.M., Schneider, T., Koushanfar, F.: Chameleon: a hybrid secure computation framework for machine learning applications. In: Proceedings of AsiaCCS (2018)","DOI":"10.1145\/3196494.3196522"},{"key":"2_CR20","doi-asserted-by":"crossref","unstructured":"Tai, R.K.H., Ma, J.P.K., Zhao, Y., Chow, S.S.M.: Privacy-preserving decision trees evaluation via linear functions. In: Proceedins of ESORICS (2017)","DOI":"10.1007\/978-3-319-66399-9_27"},{"issue":"1","key":"2_CR21","doi-asserted-by":"crossref","first-page":"266","DOI":"10.2478\/popets-2019-0015","volume":"2019","author":"A Tueno","year":"2019","unstructured":"Tueno, A., Kerschbaum, F., Katzenbeisser, S.: Private evaluation of decision trees using sublinear cost. PoPETs 2019(1), 266\u2013286 (2019)","journal-title":"PoPETs"},{"issue":"3","key":"2_CR22","doi-asserted-by":"crossref","first-page":"26","DOI":"10.2478\/popets-2019-0035","volume":"2019","author":"S Wagh","year":"2019","unstructured":"Wagh, S., Gupta, D., Chandran, N.: Securenn: efficient and private neural network training. PoPETs 2019(3), 26\u201349 (2019)","journal-title":"PoPETs"},{"key":"2_CR23","doi-asserted-by":"crossref","unstructured":"Wang, Q., Wang, J., Hu, S., Zou, Q., Ren, K.: Sechog: privacy-preserving outsourcing computation of histogram of oriented gradients in the cloud. In: Proceedings of ACM AsiaCCS (2016)","DOI":"10.1145\/2897845.2897861"},{"issue":"4","key":"2_CR24","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1515\/popets-2016-0043","volume":"2016","author":"DJ Wu","year":"2016","unstructured":"Wu, D.J., Feng, T., Naehrig, M., Lauter, K.E.: Privately evaluating decision trees and random forests. PoPETs 2016(4), 335\u2013355 (2016)","journal-title":"PoPETs"},{"key":"2_CR25","doi-asserted-by":"crossref","unstructured":"Yao, A.C.: How to generate and exchange secrets. In: Proceedings of FOCS (1986)","DOI":"10.1109\/SFCS.1986.25"},{"issue":"10","key":"2_CR26","doi-asserted-by":"publisher","first-page":"13274","DOI":"10.1016\/j.eswa.2011.04.147","volume":"38","author":"BW Yap","year":"2011","unstructured":"Yap, B.W., Ong, S., Husain, N.H.M.: Using data mining to improve assessment of credit worthiness via credit scoring models. Expert Syst. Appl. 38(10), 13274\u201313283 (2011)","journal-title":"Expert Syst. Appl."},{"issue":"6","key":"2_CR27","doi-asserted-by":"publisher","first-page":"1285","DOI":"10.1109\/TIFS.2017.2656824","volume":"12","author":"Y Zheng","year":"2017","unstructured":"Zheng, Y., Cui, H., Wang, C., Zhou, J.: Privacy-preserving image denoising from external cloud databases. IEEE Trans. Inf. Forensics Secur. 12(6), 1285\u20131298 (2017)","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"issue":"10","key":"2_CR28","doi-asserted-by":"publisher","first-page":"2475","DOI":"10.1109\/TIFS.2018.2819134","volume":"13","author":"Y Zheng","year":"2018","unstructured":"Zheng, Y., Duan, H., Wang, C.: Learning the truth privately and confidently: encrypted confidence-aware truth discovery in mobile crowdsensing. IEEE Trans. Inf. Forensics Secur. 13(10), 2475\u20132489 (2018)","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"2_CR29","doi-asserted-by":"crossref","unstructured":"Ziegeldorf, J.H., Metzke, J., R\u00fcth, J., Henze, M., Wehrle, K.: Privacy-preserving HMM forward computation. In: Proceedings of CODASPY (2017)","DOI":"10.1145\/3029806.3029816"}],"container-title":["Lecture Notes in Computer Science","Computer Security \u2013 ESORICS 2019"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-29959-0_2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,15]],"date-time":"2024-09-15T00:03:10Z","timestamp":1726358590000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-29959-0_2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030299583","9783030299590"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-29959-0_2","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"15 September 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ESORICS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Symposium on Research in Computer Security","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Luxembourg","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Luxembourg","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 September 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 September 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"esorics2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/conf.laas.fr\/esorics\/","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":"344","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":"67","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":"0","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":"19% - 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":"3,2","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":"11","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":"This content has been made available to all.","name":"free","label":"Free to read"}]}}