{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,9]],"date-time":"2026-01-09T17:20:18Z","timestamp":1767979218973,"version":"3.49.0"},"reference-count":52,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2021,3,23]],"date-time":"2021-03-23T00:00:00Z","timestamp":1616457600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,3,23]],"date-time":"2021-03-23T00:00:00Z","timestamp":1616457600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Mach Learn"],"published-print":{"date-parts":[[2021,4]]},"DOI":"10.1007\/s10994-021-05970-3","type":"journal-article","created":{"date-parts":[[2021,3,23]],"date-time":"2021-03-23T21:03:44Z","timestamp":1616533424000},"page":"675-694","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["SPEED: secure, PrivatE, and efficient deep learning"],"prefix":"10.1007","volume":"110","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5385-9447","authenticated-orcid":false,"given":"Arnaud","family":"Grivet S\u00e9bert","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rafa\u00ebl","family":"Pinot","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Martin","family":"Zuber","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"C\u00e9dric","family":"Gouy-Pailler","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Renaud","family":"Sirdey","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,3,23]]},"reference":[{"key":"5970_CR1","doi-asserted-by":"crossref","unstructured":"Abadi, M., Chu, A., Goodfellow, I., McMahan, H.B., Mironov, I., Talwar, K., & Zhang, L. (2016). Deep learning with differential privacy. In: Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, pp. 308\u2013318","DOI":"10.1145\/2976749.2978318"},{"key":"5970_CR2","doi-asserted-by":"crossref","unstructured":"\u00c1cs, G., & Castelluccia, C. (2011). I have a dream!(differentially private smart metering). In: International Workshop on Information Hiding, pp. 118\u2013132. Springer","DOI":"10.1007\/978-3-642-24178-9_9"},{"key":"5970_CR3","doi-asserted-by":"crossref","unstructured":"Aubry, P., Carpov, S., & Sirdey, R. (2019). Faster homomorphic encryption is not enough: Improved heuristic for multiplicative depth minimization of boolean circuits. In: CT-RSA, pp. 345\u2013363","DOI":"10.1007\/978-3-030-40186-3_15"},{"issue":"3","key":"5970_CR4","doi-asserted-by":"publisher","first-page":"248","DOI":"10.1109\/JIOT.2015.2412552","volume":"2","author":"H Bao","year":"2015","unstructured":"Bao, H., & Lu, R. (2015). A new differentially private data aggregation with fault tolerance for smart grid communications. IEEE Internet of Things Journal, 2(3), 248\u2013258.","journal-title":"IEEE Internet of Things Journal"},{"key":"5970_CR5","unstructured":"Beaulieu-Jones, B.K., Yuan, W., Finlayson, S.G., & Wu, Z.S. (2018). Privacy-preserving distributed deep learning for clinical data. CoRR abs\/1812.01484"},{"key":"5970_CR6","unstructured":"Bhowmick, A., Duchi, J., Freudiger, J., Kapoor, G., & Rogers, R. (2018). Protection against reconstruction and its applications in private federated learning. arXiv:1812.00984"},{"key":"5970_CR7","unstructured":"Bonawitz, K., Ivanov, V., Kreuter, B., Marcedone, A., McMahan, H.B., Patel, S., Ramage, D., Segal, A., & Seth, K. (2016). Practical secure aggregation for federated learning on user-held data. arXiv:1611.04482"},{"key":"5970_CR8","doi-asserted-by":"crossref","unstructured":"Bonawitz, K., Ivanov, V., Kreuter, B., Marcedone, A., McMahan, H.B., Patel, S., Ramage, D., Segal, A., & Seth, K. (2017). Practical secure aggregation for privacy-preserving machine learning. In: Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, pp. 1175\u20131191","DOI":"10.1145\/3133956.3133982"},{"key":"5970_CR9","unstructured":"Boura, C., Gama, N., & Georgieva, M. (2018). Chimera: A unified framework for b\/fv, tfhe and heaan fully homomorphic encryption and predictions for deep learning. Cryptology ePrint Archive, Report 2018\/758"},{"key":"5970_CR10","doi-asserted-by":"crossref","unstructured":"Brakerski, Z., Gentry, C., & Vaikuntanathan, V. (2012). (Leveled) Fully homomorphic encryption without bootstrapping. In: Proceedings of the 3rd Innovations in Theoretical Computer Science Conference, ITCS \u201912, pp. 309\u2013325","DOI":"10.1145\/2090236.2090262"},{"key":"5970_CR11","doi-asserted-by":"crossref","unstructured":"Chan, T.H.H., Shi, E., & Song, D. (2012). Privacy-preserving stream aggregation with fault tolerance. In: International Conference on Financial Cryptography and Data Security, pp. 200\u2013214. Springer","DOI":"10.1007\/978-3-642-32946-3_15"},{"key":"5970_CR12","unstructured":"Chase, M., Gilad-Bachrach, R., Laine, K., Lauter, K. E., & Rindal, P. (2017). Private collaborative neural network learning. IACR Cryptology ePrint Archive, 2017, 762."},{"key":"5970_CR13","doi-asserted-by":"crossref","unstructured":"Chillotti, I., Gama, N., Georgieva, M., & Izabach\u00e8ne, M. (2016). Faster fully homomorphic encryption: Bootstrapping in less than 0.1 seconds. In: ASIACRYPT, pp. 3\u201333","DOI":"10.1007\/978-3-662-53887-6_1"},{"key":"5970_CR14","doi-asserted-by":"crossref","unstructured":"Danezis, G., Fournet, C., Kohlweiss, M., & Zanella-B\u00e9guelin, S. (2013). Smart meter aggregation via secret-sharing. In: Proceedings of the First ACM Workshop on Smart Energy Grid Security, pp. 75\u201380","DOI":"10.1145\/2516930.2516944"},{"key":"5970_CR15","doi-asserted-by":"crossref","unstructured":"Duchi, J.C., Jordan, M.I., & Wainwright, M. J. (2013). Local privacy and statistical minimax rates. In: 2013 IEEE 54th Annual Symposium on Foundations of Computer Science, pp. 429\u2013438. IEEE","DOI":"10.1109\/FOCS.2013.53"},{"key":"5970_CR16","doi-asserted-by":"crossref","unstructured":"Dwork, C., Kenthapadi, K., McSherry, F., Mironov, I., & Naor, M. (2006). Our data, ourselves: Privacy via distributed noise generation. In: Annual International Conference on the Theory and Applications of Cryptographic Techniques, pp. 486\u2013503. Springer","DOI":"10.1007\/11761679_29"},{"key":"5970_CR17","doi-asserted-by":"crossref","unstructured":"Dwork, C., & Roth, A., et\u00a0al. (2014). The algorithmic foundations of differential privacy. Foundations and Trends\u00ae in Theoretical Computer Science 9(3\u20134), 211\u2013407","DOI":"10.1561\/0400000042"},{"key":"5970_CR18","unstructured":"Fan, J., & Vercauteren, F. (2012). Somewhat practical fully homomorphic encryption. IACR Cryptology ePrint Archive, 2012, 144."},{"key":"5970_CR19","doi-asserted-by":"crossref","unstructured":"Fiore, D., Gennaro, R., & Pastro, V. (2014). Efficiently verifiable computation on encrypted data. In: Proceedings of the 2014 ACM SIGSAC Conference on Computer and Communications Security, pp. 844\u2013855","DOI":"10.1145\/2660267.2660366"},{"key":"5970_CR20","unstructured":"Geyer, R.C., Klein, T., & Nabi, M. (2017). Differentially private federated learning: A client level perspective. arXiv:1712.07557"},{"key":"5970_CR21","unstructured":"Gilad-Bachrach, R., Dowlin, N., Laine, K., Lauter, K., Naehrig, M., & Wernsing, J. (2016). Cryptonets: Applying neural networks to encrypted data with high throughput and accuracy. In: International Conference on Machine Learning, pp. 201\u2013210"},{"issue":"5","key":"5970_CR22","doi-asserted-by":"publisher","first-page":"463","DOI":"10.1109\/TDSC.2015.2484326","volume":"14","author":"S Goryczka","year":"2015","unstructured":"Goryczka, S., & Xiong, L. (2015). A comprehensive comparison of multiparty secure additions with differential privacy. IEEE Transactions on Dependable and Secure Computing, 14(5), 463\u2013477.","journal-title":"IEEE Transactions on Dependable and Secure Computing"},{"key":"5970_CR23","doi-asserted-by":"crossref","unstructured":"Goryczka, S., Xiong, L., & Sunderam, V. (2013). Secure multiparty aggregation with differential privacy: A comparative study. In: Proceedings of the Joint EDBT\/ICDT 2013 Workshops, pp. 155\u2013163","DOI":"10.1145\/2457317.2457343"},{"key":"5970_CR24","doi-asserted-by":"crossref","unstructured":"Graepel, T., Lauter, K., & Naehrig, M. (2012). Ml confidential: Machine learning on encrypted data. In: International Conference on Information Security and Cryptology, pp. 1\u201321. Springer","DOI":"10.1007\/978-3-642-37682-5_1"},{"key":"5970_CR25","unstructured":"Hesamifard, E., Takabi, H., & Ghasemi, M. (2017). Cryptodl: Deep neural networks over encrypted data. arXiv:1711.05189"},{"key":"5970_CR26","doi-asserted-by":"crossref","unstructured":"Ishai, Y., Kilian, J., Nissim, K., & Petrank, E. (2003). Extending oblivious transfers efficiently. In: Annual International Cryptology Conference, pp. 145\u2013161. Springer","DOI":"10.1007\/978-3-540-45146-4_9"},{"key":"5970_CR27","unstructured":"Juvekar, C., Vaikuntanathan, V., & Chandrakasan, A. (2018). $$\\{$$GAZELLE$$\\}$$: A low latency framework for secure neural network inference. In: 27th $$\\{$$USENIX$$\\}$$ Security Symposium ($$\\{$$USENIX$$\\}$$ Security 18), pp. 1651\u20131669"},{"issue":"1","key":"5970_CR28","first-page":"492","volume":"17","author":"P Kairouz","year":"2016","unstructured":"Kairouz, P., Oh, S., & Viswanath, P. (2016). Extremal mechanisms for local differential privacy. The Journal of Machine Learning Research, 17(1), 492\u2013542.","journal-title":"The Journal of Machine Learning Research"},{"issue":"3","key":"5970_CR29","doi-asserted-by":"publisher","first-page":"793","DOI":"10.1137\/090756090","volume":"40","author":"SP Kasiviswanathan","year":"2011","unstructured":"Kasiviswanathan, S. P., Lee, H. K., Nissim, K., Raskhodnikova, S., & Smith, A. (2011). What can we learn privately? SIAM Journal on Computing, 40(3), 793\u2013826.","journal-title":"SIAM Journal on Computing"},{"key":"5970_CR30","unstructured":"Kotz, S., Kozubowski, T., & Podgorski, K. (2012). The Laplace distribution and generalizations: A revisit with applications to communications, economics, engineering, and finance. Springer Science & Business Media."},{"key":"5970_CR31","unstructured":"LeCun, Y. (1998). The mnist database of handwritten digits. http:\/\/yann.lecun.com\/exdb\/mnist\/"},{"key":"5970_CR32","unstructured":"Lou, Q., Feng, B., Fox, G. C., & Jiang, L. (2020). Glyph: Fast and accurately training deep neural networks on encrypted data. Advances in Neural Information Processing Systems, 33."},{"key":"5970_CR33","unstructured":"McMahan, H. B., Moore, E., Ramage, D., & Ag\u00fcera y Arcas, B. (2016). Federated learning of deep networks using model averaging. arXiv:1602.05629."},{"key":"5970_CR34","unstructured":"Netzer, Y., Wang, T., Coates, A., Bissacco, A., Wu, B., & Ng, A. Y. (2011). Reading digits in natural images with unsupervised feature learning. NIPS Workshop on Deep Learning and Unsupervised Feature Learning 2011."},{"key":"5970_CR35","unstructured":"Papernot, N., Abadi, M., Erlingsson, U., Goodfellow, I., & Talwar, K. (2017). Semi-supervised knowledge transfer for deep learning from private training data. In 5th international conference on learning representations."},{"key":"5970_CR36","unstructured":"Papernot, N., Song, S., Mironov, I., Raghunathan, A., Talwar, K., & Erlingsson, U. (2018). Scalable private learning with pate. In 6th international conference on learning representations."},{"key":"#cr-split#-5970_CR37.1","unstructured":"Parliament, E., & Council, E. (2016). Regulation"},{"key":"#cr-split#-5970_CR37.2","unstructured":"(eu) 2016\/679 of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing directive 95\/46\/ec. European Parliament and European Council: Tech. rep."},{"key":"5970_CR38","doi-asserted-by":"crossref","unstructured":"Rastogi, V., & Nath, S. (2010). Differentially private aggregation of distributed time-series with transformation and encryption. In: Proceedings of the 2010 ACM SIGMOD International Conference on Management of data (pp. 735\u2013746).","DOI":"10.1145\/1807167.1807247"},{"key":"5970_CR39","doi-asserted-by":"crossref","unstructured":"Ryffel, T., Pointcheval, D., & Bach, F. (2020). Ariann: Low-interaction privacy-preserving deep learning via function secret sharing. arXiv:2006.04593","DOI":"10.2478\/popets-2022-0015"},{"key":"5970_CR40","unstructured":"Ryffel, T., Trask, A., Dahl, M., Wagner, B., Mancuso, J., Rueckert, D., & Passerat-Palmbach, J. (2018). A generic framework for privacy preserving deep learning. arXiv:1811.04017"},{"key":"5970_CR41","unstructured":"Sabater, C., Bellet, A., & Ramon, J. (2020). Distributed differentially private averaging with improved utility and robustness to malicious parties. arXiv:2006.07218"},{"key":"5970_CR42","unstructured":"Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., & Chen, X. (2016). Improved techniques for training gans. arXiv:1606.03498"},{"key":"5970_CR43","unstructured":"Shi, E., Chan, T.H., Rieffel, E., Chow, R., & Song, D. (2011). Privacy-preserving aggregation of time-series data. In: Proc. NDSS, vol.\u00a02, pp. 1\u201317. Citeseer"},{"key":"5970_CR44","doi-asserted-by":"crossref","unstructured":"Shokri, R., & Shmatikov, V. (2015). Privacy-preserving deep learning. In: Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security, pp. 1310\u20131321","DOI":"10.1145\/2810103.2813687"},{"key":"5970_CR45","doi-asserted-by":"crossref","unstructured":"Shokri, R., Stronati, M., Song, C., & Shmatikov, V. (2017). Membership inference attacks against machine learning models. In: 2017 IEEE Symposium on Security and Privacy (SP), pp. 3\u201318. IEEE","DOI":"10.1109\/SP.2017.41"},{"key":"5970_CR46","unstructured":"Tram\u00e8r, F., Zhang, F., Juels, A., Reiter, M.K., & Ristenpart, T. (2016). Stealing machine learning models via prediction apis. In: 25th $$\\{$$USENIX$$\\}$$ Security Symposium ($$\\{$$USENIX$$\\}$$ Security 16), pp. 601\u2013618"},{"key":"5970_CR47","unstructured":"Ullman, J. (2018). Tight lower bounds for locally differentially private selection. arXiv:1802.02638"},{"key":"5970_CR48","doi-asserted-by":"crossref","unstructured":"Wang, B., & Gong, N. Z. (2018). Stealing hyperparameters in machine learning. In: 2018 IEEE Symposium on Security and Privacy (SP), pp. 36\u201352. IEEE","DOI":"10.1109\/SP.2018.00038"},{"key":"5970_CR49","doi-asserted-by":"crossref","unstructured":"Wu, X., Fredrikson, M., Jha, S., & Naughton, J. F. (2016). A methodology for formalizing model-inversion attacks. In: 2016 IEEE 29th Computer Security Foundations Symposium (CSF), pp. 355\u2013370. IEEE","DOI":"10.1109\/CSF.2016.32"},{"key":"5970_CR50","unstructured":"Yan, M., Fletcher, C.W., & Torrellas, J. (2018). Cache telepathy: Leveraging shared resource attacks to learn DNN architectures. CoRR abs\/1808.04761"},{"key":"5970_CR51","doi-asserted-by":"crossref","unstructured":"Zuber, M., Carpov, S., & Sirdey, R. (2020). Towards real-time hidden speaker recognition by means of fully homomorphic encryption. In: International Conference on Information and Communications Security, pp. 403\u2013421. Springer","DOI":"10.1007\/978-3-030-61078-4_23"}],"container-title":["Machine Learning"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10994-021-05970-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10994-021-05970-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10994-021-05970-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,3,23]],"date-time":"2022-03-23T01:05:56Z","timestamp":1647997556000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10994-021-05970-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,3,23]]},"references-count":52,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2021,4]]}},"alternative-id":["5970"],"URL":"https:\/\/doi.org\/10.1007\/s10994-021-05970-3","relation":{},"ISSN":["0885-6125","1573-0565"],"issn-type":[{"value":"0885-6125","type":"print"},{"value":"1573-0565","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,3,23]]},"assertion":[{"value":"22 November 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 January 2021","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 March 2021","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 March 2021","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"The MNIST\u00a0(LeCun ) and SVHN\u00a0(Netzer et\u00a0al. ) datasets can be found respectively at  and .","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Availability of data and material"}},{"value":"The source codes used to run the experiments and compute the DP guarantees can be accessed on .","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Code availability"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}