{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,11]],"date-time":"2026-07-11T16:44:21Z","timestamp":1783788261905,"version":"3.55.0"},"publisher-location":"Cham","reference-count":38,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030306182","type":"print"},{"value":"9783030306199","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":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1007\/978-3-030-30619-9_26","type":"book-chapter","created":{"date-parts":[[2019,9,11]],"date-time":"2019-09-11T06:51:47Z","timestamp":1568184707000},"page":"361-377","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Secure and Fast Decision Tree Evaluation on Outsourced Cloud Data"],"prefix":"10.1007","author":[{"given":"Lin","family":"Liu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinshu","family":"Su","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rongmao","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinrong","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guangliang","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jie","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2019,9,9]]},"reference":[{"key":"26_CR1","unstructured":"The health insurance portability and accountability act of privacy and security rules. \nhttp:\/\/www.hhs.gov\/ocr\/privacy"},{"key":"26_CR2","doi-asserted-by":"crossref","unstructured":"Singh, A., Guttag, J.V.: A comparison of non-symmetric entropy-based classification trees and support vector machine for cardiovascular risk stratification, pp. 79\u201382 (2011)","DOI":"10.1109\/IEMBS.2011.6089901"},{"issue":"23","key":"26_CR3","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(23), 2387\u20132403 (2013)","journal-title":"Neural Comput. Appl."},{"issue":"1","key":"26_CR4","first-page":"96","volume":"1","author":"HC Koh","year":"2006","unstructured":"Koh, H.C., Tan, W.C., Goh, C.P.: A two-step method to construct credit scoring models with data mining techniques. Int. J. Bus. Inf. 1(1), 96\u2013118 (2006)","journal-title":"Int. J. Bus. Inf."},{"issue":"3","key":"26_CR5","doi-asserted-by":"publisher","first-page":"801","DOI":"10.1007\/s10579-017-9406-7","volume":"52","author":"A Rago","year":"2018","unstructured":"Rago, A., Marcos, C., Diaz-Pace, J.A.: Using semantic roles to improve text classification in the requirements domain. Lang. Resour. Eval. 52(3), 801\u2013837 (2018)","journal-title":"Lang. Resour. Eval."},{"key":"26_CR6","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1007\/3-540-44598-6_3","volume-title":"Advances in Cryptology \u2014 CRYPTO 2000","author":"Y Lindell","year":"2000","unstructured":"Lindell, Y., Pinkas, B.: Privacy preserving data mining. In: Bellare, M. (ed.) CRYPTO 2000. LNCS, vol. 1880, pp. 36\u201354. Springer, Heidelberg (2000). \nhttps:\/\/doi.org\/10.1007\/3-540-44598-6_3"},{"key":"26_CR7","doi-asserted-by":"crossref","unstructured":"Agrawal, R., Srikant, R.: Privacy-preserving data mining. In: ACM SIGMOD Record, vol. 29, pp. 439\u2013450. ACM (2000)","DOI":"10.1145\/335191.335438"},{"key":"26_CR8","doi-asserted-by":"crossref","unstructured":"Bost, R., Popa, R.A., Tu, S., Goldwasser, S.: Machine learning classification over encrypted data. In NDSS, vol. 4324, p. 4325 (2015)","DOI":"10.14722\/ndss.2015.23241"},{"issue":"4","key":"26_CR9","doi-asserted-by":"publisher","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.: Privately evaluating decision trees and random forests. Proc. Priv. Enhanc. Technol. 2016(4), 335\u2013355 (2016)","journal-title":"Proc. Priv. Enhanc. Technol."},{"key":"26_CR10","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"494","DOI":"10.1007\/978-3-319-66399-9_27","volume-title":"Computer Security \u2013 ESORICS 2017","author":"RKH Tai","year":"2017","unstructured":"Tai, R.K.H., Ma, J.P.K., Zhao, Y., Chow, S.S.M.: Privacy-preserving decision trees evaluation via linear functions. In: Foley, S.N., Gollmann, D., Snekkenes, E. (eds.) ESORICS 2017. LNCS, vol. 10493, pp. 494\u2013512. Springer, Cham (2017). \nhttps:\/\/doi.org\/10.1007\/978-3-319-66399-9_27"},{"key":"26_CR11","unstructured":"Du, W., Zhan, Z.: Building decision tree classifier on private data. In IEEE International Conference on Privacy, Security and Data Mining (2002)"},{"key":"26_CR12","doi-asserted-by":"publisher","first-page":"507","DOI":"10.1016\/j.ins.2018.12.015","volume":"481","author":"X Ma","year":"2019","unstructured":"Ma, X., Chen, X., Zhang, X.: Non-interactive privacy-preserving neural network prediction. Inf. Sci. 481, 507\u2013519 (2019)","journal-title":"Inf. Sci."},{"key":"26_CR13","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1016\/j.ins.2018.05.005","volume":"459","author":"X Ma","year":"2018","unstructured":"Ma, X., Zhang, F., Chen, X., Shen, J.: Privacy preserving multi-party computation delegation for deep learning in cloud computing. Inf. Sci. 459, 103\u2013116 (2018)","journal-title":"Inf. Sci."},{"issue":"3","key":"26_CR14","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1109\/MNET.2019.1800362","volume":"33","author":"Y Yong","year":"2019","unstructured":"Yong, Y., Li, H., Chen, R., Zhao, Y., Yang, H., Xiaojiang, D.: Enabling secure intelligent network with cloud-assisted privacy-preserving machine learning. IEEE Netw. 33(3), 82\u201387 (2019)","journal-title":"IEEE Netw."},{"key":"26_CR15","doi-asserted-by":"crossref","unstructured":"Brickell, J., Porter, D.E., Shmatikov, V., Witchel, E.: Privacy-preserving remote diagnostics. In: Proceedings of the 14th ACM Conference on Computer and Communications Security, pp. 498\u2013507. ACM (2007)","DOI":"10.1145\/1315245.1315307"},{"key":"26_CR16","unstructured":"Yao, A.C.-C.: How to generate and exchange secrets. In: 27th Annual Symposium on Foundations of Computer Science, pp. 162\u2013167. IEEE (19860)"},{"key":"26_CR17","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"424","DOI":"10.1007\/978-3-642-04444-1_26","volume-title":"Computer Security \u2013 ESORICS 2009","author":"M Barni","year":"2009","unstructured":"Barni, M., Failla, P., Kolesnikov, V., Lazzeretti, R., Sadeghi, A.-R., Schneider, T.: Secure evaluation of private linear branching programs with medical applications. In: Backes, M., Ning, P. (eds.) ESORICS 2009. LNCS, vol. 5789, pp. 424\u2013439. Springer, Heidelberg (2009). \nhttps:\/\/doi.org\/10.1007\/978-3-642-04444-1_26"},{"issue":"1","key":"26_CR18","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1504\/IJACT.2008.017048","volume":"1","author":"I Damgard","year":"2008","unstructured":"Damgard, I., Geisler, M., Kroigard, M.: Homomorphic encryption and secure comparison. Int. J. Appl. Crypt. 1(1), 22\u201331 (2008)","journal-title":"Int. J. Appl. Crypt."},{"key":"26_CR19","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"275","DOI":"10.1007\/978-3-642-39884-1_23","volume-title":"Financial Cryptography and Data Security","author":"T Schneider","year":"2013","unstructured":"Schneider, T., Zohner, M.: GMW vs. Yao? efficient secure two-party computation with low depth circuits. In: Sadeghi, A.-R. (ed.) FC 2013. LNCS, vol. 7859, pp. 275\u2013292. Springer, Heidelberg (2013). \nhttps:\/\/doi.org\/10.1007\/978-3-642-39884-1_23"},{"key":"26_CR20","doi-asserted-by":"publisher","first-page":"217","DOI":"10.1109\/TDSC.2017.2679189","volume":"16","author":"M Cock De","year":"2017","unstructured":"De Cock, M., 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, 217\u2013230 (2017)","journal-title":"IEEE Trans. Dependable Secure Comput."},{"key":"26_CR21","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). \nhttps:\/\/doi.org\/10.1007\/978-3-319-95729-6_16"},{"issue":"1","key":"26_CR22","doi-asserted-by":"publisher","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. Proc. Priv. Enhanc. Technol. 2019(1), 266\u2013286 (2019)","journal-title":"Proc. Priv. Enhanc. Technol."},{"key":"26_CR23","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"223","DOI":"10.1007\/3-540-48910-X_16","volume-title":"Advances in Cryptology \u2014 EUROCRYPT 1999","author":"P Paillier","year":"1999","unstructured":"Paillier, P.: Public-key cryptosystems based on composite degree residuosity classes. In: Stern, J. (ed.) EUROCRYPT 1999. LNCS, vol. 1592, pp. 223\u2013238. Springer, Heidelberg (1999). \nhttps:\/\/doi.org\/10.1007\/3-540-48910-X_16"},{"issue":"2","key":"26_CR24","doi-asserted-by":"publisher","first-page":"265","DOI":"10.1007\/s00145-017-9275-7","volume":"32","author":"C Hazay","year":"2019","unstructured":"Hazay, C., Mikkelsen, G.L., Rabin, T., Toft, T., Nicolosi, A.A.: Efficient RSA key generation and threshold Paillier in the two-party setting. J. Cryptol. 32(2), 265\u2013323 (2019)","journal-title":"J. Cryptol."},{"issue":"11","key":"26_CR25","doi-asserted-by":"publisher","first-page":"612","DOI":"10.1145\/359168.359176","volume":"22","author":"A Shamir","year":"1979","unstructured":"Shamir, A.: How to share a secret. Commun. ACM 22(11), 612\u2013613 (1979)","journal-title":"Commun. ACM"},{"key":"26_CR26","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). \nhttps:\/\/doi.org\/10.1007\/3-540-46766-1_34"},{"key":"26_CR27","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 the 2018 on Asia Conference on Computer and Communications Security, pp. 707\u2013721. ACM (2018)","DOI":"10.1145\/3196494.3196522"},{"key":"26_CR28","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1109\/TDSC.2016.2536601","volume":"15","author":"X Liu","year":"2016","unstructured":"Liu, X., Choo, R., Deng, R., Lu, R., Weng, J.: Efficient and privacy-preserving outsourced calculation of rational numbers. IEEE Trans. Dependable Secure Comput. 15, 27\u201339 (2016)","journal-title":"IEEE Trans. Dependable Secure Comput."},{"key":"26_CR29","doi-asserted-by":"crossref","unstructured":"Elmehdwi, Y., Samanthula, B.K., Jiang, W.: Secure k-nearest neighbor query over encrypted data in outsourced environments. In: 2014 IEEE 30th International Conference on Data Engineering (ICDE), pp. 664\u2013675. IEEE (2014)","DOI":"10.1109\/ICDE.2014.6816690"},{"key":"26_CR30","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"431","DOI":"10.1007\/978-3-319-93638-3_25","volume-title":"Information Security and Privacy","author":"L Liu","year":"2018","unstructured":"Liu, L., et al.: Privacy-preserving mining of association rule on outsourced cloud data from multiple parties. In: Susilo, W., Yang, G. (eds.) ACISP 2018. LNCS, vol. 10946, pp. 431\u2013451. Springer, Cham (2018). \nhttps:\/\/doi.org\/10.1007\/978-3-319-93638-3_25"},{"issue":"4","key":"26_CR31","first-page":"708","volume":"15","author":"D Wang","year":"2016","unstructured":"Wang, D., Wang, P.: Two birds with one stone: two-factor authentication with security beyond conventional bound. IEEE Trans. Dependable Secure Comput. 15(4), 708\u2013722 (2016)","journal-title":"IEEE Trans. Dependable Secure Comput."},{"issue":"1","key":"26_CR32","doi-asserted-by":"publisher","first-page":"916","DOI":"10.1109\/JSYST.2016.2585681","volume":"12","author":"D Wang","year":"2016","unstructured":"Wang, D., Cheng, H., He, D., Wang, P.: On the challenges in designing identity-based privacy-preserving authentication schemes for mobile devices. IEEE Syst. J. 12(1), 916\u2013925 (2016)","journal-title":"IEEE Syst. J."},{"issue":"11","key":"26_CR33","doi-asserted-by":"publisher","first-page":"2401","DOI":"10.1109\/TIFS.2016.2573770","volume":"11","author":"X Liu","year":"2016","unstructured":"Liu, X., Deng, R.H., Choo, K.-K.R., Weng, J.: An efficient privacy-preserving outsourced calculation toolkit with multiple keys. IEEE Trans. Inf. Forensics Secur. 11(11), 2401\u20132414 (2016)","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"26_CR34","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: 2013 IEEE Symposium on Security and Privacy (SP), pp. 334\u2013348. IEEE (2013)","DOI":"10.1109\/SP.2013.30"},{"key":"26_CR35","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"285","DOI":"10.1007\/11681878_15","volume-title":"Theory of Cryptography","author":"I Damg\u00e5rd","year":"2006","unstructured":"Damg\u00e5rd, I., Fitzi, M., Kiltz, E., Nielsen, J.B., Toft, T.: Unconditionally secure constant-rounds multi-party computation for equality, comparison, bits and exponentiation. In: Halevi, S., Rabin, T. (eds.) TCC 2006. LNCS, vol. 3876, pp. 285\u2013304. Springer, Heidelberg (2006). \nhttps:\/\/doi.org\/10.1007\/11681878_15"},{"key":"26_CR36","doi-asserted-by":"crossref","unstructured":"Huang, K., Liu, X., Fu, S., Guo, D., Xu, M.: A lightweight privacy-preserving CNN feature extraction framework for mobile sensing. IEEE Trans. Dependable Secure Comput. (2019)","DOI":"10.1109\/TDSC.2019.2913362"},{"key":"26_CR37","volume-title":"Foundations of Cryptography: Volume 2, Basic Applications","author":"O Goldreich","year":"2009","unstructured":"Goldreich, O.: Foundations of Cryptography: Volume 2, Basic Applications. Cambridge University Press, Cambridge (2009)"},{"key":"26_CR38","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"192","DOI":"10.1007\/978-3-540-88313-5_13","volume-title":"Computer Security - ESORICS 2008","author":"D Bogdanov","year":"2008","unstructured":"Bogdanov, D., Laur, S., Willemson, J.: Sharemind: a framework for fast privacy-preserving computations. In: Jajodia, S., Lopez, J. (eds.) ESORICS 2008. LNCS, vol. 5283, pp. 192\u2013206. Springer, Heidelberg (2008). \nhttps:\/\/doi.org\/10.1007\/978-3-540-88313-5_13"}],"container-title":["Lecture Notes in Computer Science","Machine Learning for Cyber Security"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-30619-9_26","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,3,17]],"date-time":"2020-03-17T00:08:48Z","timestamp":1584403728000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-30619-9_26"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030306182","9783030306199"],"references-count":38,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-30619-9_26","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":"9 September 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ML4CS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Machine Learning for Cyber Security","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Xi'an","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","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":"19 September 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 September 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ml4cs2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/ml4cs2019.xidian.edu.cn\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-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":"70","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":"23","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":"3","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":"33% - 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":"3","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}