{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T18:57:03Z","timestamp":1772909823495,"version":"3.50.1"},"publisher-location":"Cham","reference-count":19,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031587337","type":"print"},{"value":"9783031587344","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-3-031-58734-4_1","type":"book-chapter","created":{"date-parts":[[2024,4,30]],"date-time":"2024-04-30T15:12:08Z","timestamp":1714489928000},"page":"3-33","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Polynomial Time Cryptanalytic Extraction of\u00a0Neural Network Models"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0776-5060","authenticated-orcid":false,"given":"Isaac A.","family":"Canales-Mart\u00ednez","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7006-1779","authenticated-orcid":false,"given":"Jorge","family":"Ch\u00e1vez-Saab","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5357-832X","authenticated-orcid":false,"given":"Anna","family":"Hambitzer","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5916-6625","authenticated-orcid":false,"given":"Francisco","family":"Rodr\u00edguez-Henr\u00edquez","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5400-040X","authenticated-orcid":false,"given":"Nitin","family":"Satpute","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5422-905X","authenticated-orcid":false,"given":"Adi","family":"Shamir","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,5,1]]},"reference":[{"key":"1_CR1","unstructured":"Abadi, M., et\u00a0al.: Tensorflow: a system for large-scale machine learning. In: 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16), pp. 265\u2013283 (2016)"},{"key":"1_CR2","unstructured":"Batina, L., Bhasin, S., Jap, D., Picek, S.: CSI NN: reverse engineering of neural network architectures through electromagnetic side channel. In: Heninger, N., Traynor, P. (eds.) 28th USENIX Security Symposium, USENIX Security 2019, Santa Clara, CA, USA, August 14-16, 2019, pp. 515\u2013532. USENIX Association (2019)"},{"key":"1_CR3","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1007\/3-540-56483-7_20","volume-title":"Machine Learning: From Theory to Applications","author":"AL Blum","year":"1993","unstructured":"Blum, A.L., Rivest, R.L.: Training a 3-node neural network is NP-complete. In: Hanson, S.J., Remmele, W., Rivest, R.L. (eds.) Machine Learning: From Theory to Applications. LNCS, vol. 661, pp. 9\u201328. Springer, Heidelberg (1993). https:\/\/doi.org\/10.1007\/3-540-56483-7_20"},{"key":"1_CR4","doi-asserted-by":"crossref","unstructured":"Canales-Mart\u00ednez, I.A., et al.: Polynomial time cryptanalytic extraction of neural network models. Cryptology ePrint Archive (2023)","DOI":"10.1007\/978-3-031-58734-4_1"},{"key":"1_CR5","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"189","DOI":"10.1007\/978-3-030-56877-1_7","volume-title":"Advances in Cryptology \u2013 CRYPTO 2020","author":"N Carlini","year":"2020","unstructured":"Carlini, N., Jagielski, M., Mironov, I.: Cryptanalytic extraction of neural\u00a0network models. In: Micciancio, D., Ristenpart, T. (eds.) CRYPTO 2020. LNCS, vol. 12172, pp. 189\u2013218. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-56877-1_7"},{"key":"1_CR6","unstructured":"Daniely, A., Granot, E.: An exact poly-time membership-queries algorithm for extraction a three-layer relu network. CoRR abs\/2105.09673 (2021). https:\/\/arxiv.org\/abs\/2105.09673"},{"key":"1_CR7","unstructured":"Dosovitskiy, A., et\u00a0al.: An image is worth 16x16 words: transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)"},{"issue":"3","key":"1_CR8","doi-asserted-by":"publisher","first-page":"507","DOI":"10.4171\/rmi\/160","volume":"10","author":"C Fefferman","year":"1994","unstructured":"Fefferman, C.: Reconstructing a neural net from its output. Revista Matem\u00e1tica Iberoamericana 10(3), 507\u2013555 (1994). http:\/\/eudml.org\/doc\/39464","journal-title":"Revista Matem\u00e1tica Iberoamericana"},{"key":"1_CR9","unstructured":"Jagielski, M., Carlini, N., Berthelot, D., Kurakin, A., Papernot, N.: High accuracy and high fidelity extraction of neural networks. In: 29th USENIX Security Symposium (USENIX Security 20), pp. 1345\u20131362 (2020)"},{"key":"1_CR10","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1007\/978-3-031-25319-5_3","volume-title":"CARDIS 2022","author":"R Joud","year":"2022","unstructured":"Joud, R., Mo\u00ebllic, P.A., Ponti\u00e9, S., Rigaud, J.B.: A practical introduction to side-channel extraction of deep neural network parameters. In: Buhan, I., Schneider, T. (eds.) CARDIS 2022. LNCS, vol. 13820, pp. 45\u201365. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-25319-5_3"},{"key":"1_CR11","unstructured":"Lin, Z., Memisevic, R., Konda, K.: How far can we go without convolution: improving fully-connected networks. arXiv preprint arXiv:1511.02580 (2015)"},{"key":"1_CR12","doi-asserted-by":"crossref","unstructured":"Lowd, D., Meek, C.: Adversarial learning. In: Grossman, R., Bayardo, R.J., Bennett, K.P. (eds.) Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Chicago, Illinois, USA, August 21\u201324, 2005. pp. 641\u2013647. ACM (2005)","DOI":"10.1145\/1081870.1081950"},{"key":"1_CR13","doi-asserted-by":"crossref","unstructured":"Milli, S., Schmidt, L., Dragan, A.D., Hardt, M.: Model reconstruction from model explanations. In: danah boyd, Morgenstern, J.H. (eds.) Proceedings of the Conference on Fairness, Accountability, and Transparency, FAT* 2019, Atlanta, GA, USA, January 29\u201331, 2019. pp.\u00a01\u20139. ACM (2019)","DOI":"10.1145\/3287560.3287562"},{"issue":"14s","key":"1_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3595292","volume":"55","author":"D Oliynyk","year":"2023","unstructured":"Oliynyk, D., Mayer, R., Rauber, A.: I know what you trained last summer: a survey on stealing machine learning models and defences. ACM Comput. Surv. 55(14s), 1\u201341 (2023)","journal-title":"ACM Comput. Surv."},{"key":"1_CR15","doi-asserted-by":"crossref","unstructured":"Papernot, N., McDaniel, P.D., Goodfellow, I.J., Jha, S., Celik, Z.B., Swami, A.: Practical black-box attacks against machine learning. In: Karri, R., Sinanoglu, O., Sadeghi, A., Yi, X. (eds.) Proceedings of the 2017 ACM on Asia Conference on Computer and Communications Security, AsiaCCS 2017, Abu Dhabi, United Arab Emirates, April 2\u20136, 2017. pp. 506\u2013519. ACM (2017)","DOI":"10.1145\/3052973.3053009"},{"key":"1_CR16","doi-asserted-by":"crossref","unstructured":"Reith, R.N., Schneider, T., Tkachenko, O.: Efficiently stealing your machine learning models. In: Cavallaro, L., Kinder, J., Domingo-Ferrer, J. (eds.) Proceedings of the 18th ACM Workshop on Privacy in the Electronic Society, WPES@CCS 2019, London, UK, November 11, 2019, pp. 198\u2013210. ACM (2019)","DOI":"10.1145\/3338498.3358646"},{"key":"1_CR17","unstructured":"Rolnick, D., K\u00f6rding, K.P.: Reverse-engineering deep relu networks. In: Proceedings of the 37th International Conference on Machine Learning, ICML 2020, 13\u201318 July 2020, Virtual Event. Proceedings of Machine Learning Research, vol.\u00a0119, pp. 8178\u20138187. PMLR (2020)"},{"issue":"11","key":"1_CR18","doi-asserted-by":"publisher","first-page":"1958","DOI":"10.1109\/TPAMI.2008.128","volume":"30","author":"A Torralba","year":"2008","unstructured":"Torralba, A., Fergus, R., Freeman, W.T.: 80 million tiny images: a large data set for nonparametric object and scene recognition. IEEE Trans. Pattern Anal. Mach. Intell. 30(11), 1958\u20131970 (2008)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"1_CR19","unstructured":"Tram\u00e8r, F., Zhang, F., Juels, A., Reiter, M.K., Ristenpart, T.: Stealing machine learning models via prediction APIs. In: 25th USENIX Security Symposium (USENIX Security 16), pp. 601\u2013618 (2016)"}],"container-title":["Lecture Notes in Computer Science","Advances in Cryptology \u2013 EUROCRYPT 2024"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-58734-4_1","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,12]],"date-time":"2024-05-12T23:05:25Z","timestamp":1715555125000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-58734-4_1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031587337","9783031587344"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-58734-4_1","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"1 May 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"EUROCRYPT","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Annual International Conference on the Theory and Applications of Cryptographic Techniques","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Zurich","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Switzerland","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 May 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 May 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"43","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eurocrypt2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eurocrypt.iacr.org\/2024\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}