{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T04:31:30Z","timestamp":1743049890866,"version":"3.40.3"},"publisher-location":"Cham","reference-count":21,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030622220"},{"type":"electronic","value":"9783030622237"}],"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\/978-3-030-62223-7_22","type":"book-chapter","created":{"date-parts":[[2020,11,10]],"date-time":"2020-11-10T10:03:00Z","timestamp":1605002580000},"page":"262-271","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Towards Privacy-Preserving Aggregated Prediction from SPDZ"],"prefix":"10.1007","author":[{"given":"Qi","family":"Zhao","sequence":"first","affiliation":[]},{"given":"Bo","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Anli","family":"Yan","sequence":"additional","affiliation":[]},{"given":"Shan","family":"Jing","sequence":"additional","affiliation":[]},{"given":"Chuan","family":"Zhao","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,11,11]]},"reference":[{"key":"22_CR1","doi-asserted-by":"crossref","unstructured":"Zander, S., Nguyen, T., Armitage, G.: Automated traffic classification and application identification using machine learning. In: The IEEE Conference on Local Computer Networks 30th Anniversary (LCN 2005) l, pp. 250\u2013257. IEEE (2005)","DOI":"10.1109\/LCN.2005.35"},{"key":"22_CR2","doi-asserted-by":"crossref","unstructured":"Taigman, Y., Yang, M., Ranzato, M.A., Wolf, L.: Deepface: closing the gap to human-level performance in face verification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 1701\u20131708 (2014)","DOI":"10.1109\/CVPR.2014.220"},{"key":"22_CR3","doi-asserted-by":"crossref","unstructured":"Sundermeyer, M., Schl\u00fcter, R., Ney, H.: LSTM neural networks for language modeling. In: Thirteenth Annual Conference of the International Speech Communication Association (2012)","DOI":"10.21437\/Interspeech.2012-65"},{"key":"22_CR4","doi-asserted-by":"crossref","unstructured":"Papernot, N., McDaniel, P., Sinha, A., Wellman, M.P.: Sok: security and privacy in machine learning. In: 2018 IEEE European Symposium on Security and Privacy (EuroS&P), pp. 399\u2013414. IEEE (2018)","DOI":"10.1109\/EuroSP.2018.00035"},{"key":"22_CR5","doi-asserted-by":"crossref","unstructured":"Shokri, R., Shmatikov, V.: Privacy-preserving deep learning. In: Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security, pp. 1310\u20131321. ACM (2015)","DOI":"10.1145\/2810103.2813687"},{"key":"22_CR6","unstructured":"McMahan, H.B., Moore, E., Ramage, D., Hampson, S., et al.: Communication-efficient learning of deep networks from decentralized data. arXiv preprint arXiv:1602.05629 (2016)"},{"key":"22_CR7","unstructured":"McMahan, B., Ramage, D.: Federated learning: collaborative machine learning without centralized training data. Google Research Blog, 3 (2017)"},{"key":"22_CR8","doi-asserted-by":"crossref","unstructured":"Hitaj, B., Ateniese, G., P\u00e9rez-Cruz, F.: Deep models under the gan: information leakage from collaborative deep learning. In: Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, pp. 603\u2013618. ACM (2017)","DOI":"10.1145\/3133956.3134012"},{"key":"22_CR9","doi-asserted-by":"crossref","unstructured":"Melis, L., Song, C., De Cristofaro, E., Shmatikov, V.: Exploiting unintended feature leakage in collaborative learning. In 2019 IEEE Symposium on Security and Privacy (SP), pp. 691\u2013706. IEEE (2019)","DOI":"10.1109\/SP.2019.00029"},{"key":"22_CR10","unstructured":"Zhu, L., Liu, Z., Han, S.: Deep leakage from gradients. In: Advances in Neural Information Processing Systems, pp. 14747\u201314756 (2019)"},{"key":"22_CR11","doi-asserted-by":"crossref","unstructured":"Fredrikson, M., Jha, S., Ristenpart, T.: Model inversion attacks that exploit confidence information and basic countermeasures. In: Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security, pp. 1322\u20131333. ACM (2015)","DOI":"10.1145\/2810103.2813677"},{"key":"22_CR12","doi-asserted-by":"crossref","unstructured":"Shokri, R., Stronati, M., Song, C., Shmatikov, V.: Membership inference attacks against machine learning models. In: 2017 IEEE Symposium on Security and Privacy (SP), pp. 3\u201318. IEEE (2017)","DOI":"10.1109\/SP.2017.41"},{"key":"22_CR13","unstructured":"Papernot, N., Abadi, M., Erlingsson, \u00da., Goodfellow, I., Talwar, K.: Semi-supervised knowledge transfer for deep learning from private training data (2017)"},{"key":"22_CR14","doi-asserted-by":"crossref","unstructured":"Mohassel, P., Zhang, Y.: Secureml: a system for scalable privacy-preserving machine learning. In: 2017 IEEE Symposium on Security and Privacy (SP), pp. 19\u201338. IEEE (2017)","DOI":"10.1109\/SP.2017.12"},{"key":"22_CR15","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"643","DOI":"10.1007\/978-3-642-32009-5_38","volume-title":"Advances in Cryptology \u2013 CRYPTO 2012","author":"I Damg\u00e5rd","year":"2012","unstructured":"Damg\u00e5rd, I., Pastro, V., Smart, N., Zakarias, S.: Multiparty computation from somewhat homomorphic encryption. In: Safavi-Naini, R., Canetti, R. (eds.) CRYPTO 2012. LNCS, vol. 7417, pp. 643\u2013662. Springer, Heidelberg (2012). https:\/\/doi.org\/10.1007\/978-3-642-32009-5_38"},{"key":"22_CR16","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/978-3-642-40203-6_1","volume-title":"Computer Security \u2013 ESORICS 2013","author":"I Damg\u00e5rd","year":"2013","unstructured":"Damg\u00e5rd, I., Keller, M., Larraia, E., Pastro, V., Scholl, P., Smart, N.P.: Practical covertly secure MPC for dishonest majority \u2013 or: breaking the SPDZ limits. In: Crampton, J., Jajodia, S., Mayes, K. (eds.) ESORICS 2013. LNCS, vol. 8134, pp. 1\u201318. Springer, Heidelberg (2013). https:\/\/doi.org\/10.1007\/978-3-642-40203-6_1"},{"key":"22_CR17","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\u2014CRYPTO 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":"22_CR18","unstructured":"Dahl, M., et al.:. Private machine learning in tensorflow using secure computation. arXiv preprint arXiv:1810.08130 (2018)"},{"key":"22_CR19","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/978-3-540-79228-4_1","volume-title":"Theory and Applications of Models of Computation","author":"C Dwork","year":"2008","unstructured":"Dwork, C.: Differential privacy: a survey of results. In: Agrawal, M., Du, D., Duan, Z., Li, A. (eds.) TAMC 2008. LNCS, vol. 4978, pp. 1\u201319. Springer, Heidelberg (2008). https:\/\/doi.org\/10.1007\/978-3-540-79228-4_1"},{"key":"22_CR20","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/11787006_1","volume-title":"Automata, Languages and Programming","author":"C Dwork","year":"2006","unstructured":"Dwork, C.: Differential privacy. In: Bugliesi, M., Preneel, B., Sassone, V., Wegener, I. (eds.) ICALP 2006. LNCS, vol. 4052, pp. 1\u201312. Springer, Heidelberg (2006). https:\/\/doi.org\/10.1007\/11787006_1"},{"key":"22_CR21","doi-asserted-by":"crossref","unstructured":"Abadi, M., et al.:. Deep learning with differential privacy. In: Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, pp. 308\u2013318 (2016)","DOI":"10.1145\/2976749.2978318"}],"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\/978-3-030-62223-7_22","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,27]],"date-time":"2022-11-27T13:25:45Z","timestamp":1669555545000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-62223-7_22"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030622220","9783030622237"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-62223-7_22","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"11 November 2020","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":"Guangzhou","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":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 October 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 October 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ml4cs2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/nsclab.org\/ml4cs2020\/index.html","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":"360","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":"118","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":"40","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":"2.2","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":"8","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)"}}]}}