{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T19:33:16Z","timestamp":1743017596585,"version":"3.40.3"},"publisher-location":"Cham","reference-count":39,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031223891"},{"type":"electronic","value":"9783031223907"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"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":[[2022]]},"DOI":"10.1007\/978-3-031-22390-7_25","type":"book-chapter","created":{"date-parts":[[2022,12,9]],"date-time":"2022-12-09T00:03:46Z","timestamp":1670544226000},"page":"422-442","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["HeHe: Balancing the\u00a0Privacy and\u00a0Efficiency in\u00a0Training CNNs over\u00a0the\u00a0Semi-honest Cloud"],"prefix":"10.1007","author":[{"given":"Longlong","family":"Sun","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8310-7169","authenticated-orcid":false,"given":"Hui","family":"Li","sequence":"additional","affiliation":[]},{"given":"Shiwen","family":"Yu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0764-3741","authenticated-orcid":false,"given":"Xindi","family":"Ma","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3210-0714","authenticated-orcid":false,"given":"Yanguo","family":"Peng","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5569-0780","authenticated-orcid":false,"given":"Jiangtao","family":"Cui","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,12,9]]},"reference":[{"key":"25_CR1","doi-asserted-by":"crossref","unstructured":"Acar, A., Aksu, H., Uluagac, A.S., Conti, M.: A survey on homomorphic encryption schemes: theory and implementation. ACM Comput. Surv. 51(4), 79:1\u201379:35 (2018)","DOI":"10.1145\/3214303"},{"key":"25_CR2","doi-asserted-by":"crossref","unstructured":"Bost, R., Popa, R.A., Tu, S., Goldwasser, S.: Machine learning classification over encrypted data. In: Proceedings of 22nd Annual Network and Distributed System Security Symposium (2015)","DOI":"10.14722\/ndss.2015.23241"},{"key":"25_CR3","doi-asserted-by":"crossref","unstructured":"Boureau, Y., Bach, F.R., LeCun, Y., Ponce, J.: Learning mid-level features for recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2559\u20132566 (2010)","DOI":"10.1109\/CVPR.2010.5539963"},{"key":"25_CR4","doi-asserted-by":"crossref","unstructured":"Bourse, F., Minelli, M., Minihold, M., Paillier, P.: Fast homomorphic evaluation of deep discretized neural networks. In: Proceedings of 38th Annual International Cryptology Conference on Advances in Cryptology, pp. 483\u2013512 (2018)","DOI":"10.1007\/978-3-319-96878-0_17"},{"key":"25_CR5","unstructured":"Central Bureau of the Commission Internationale de l\u2019\u00c9clairage (Vienna, Austria): Cie (1978) recommendations on uniform color spaces, color-difference equations, and metric color terms. Supplement 2 to CIE publication 15 (E1.3.1) 1971\/(TC1.3) (1978)"},{"key":"25_CR6","first-page":"35","volume":"2017","author":"H Chabanne","year":"2017","unstructured":"Chabanne, H., de Wargny, A., Milgram, J., Morel, C., Prouff, E.: Privacy-preserving classification on deep neural network. IACR Cryptology ePrint Archive 2017, 35 (2017)","journal-title":"IACR Cryptology ePrint Archive"},{"key":"25_CR7","unstructured":"Chou, E., Beal, J., Levy, D., Yeung, S., Haque, A., Fei-Fei, L.: Faster cryptonets: leveraging sparsity for real-world encrypted inference. arXiv:1811.09953 (2018)"},{"key":"25_CR8","doi-asserted-by":"crossref","unstructured":"Dosovitskiy, A., Brox, T.: Inverting visual representations with convolutional networks. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 4829\u20134837 (2016)","DOI":"10.1109\/CVPR.2016.522"},{"key":"25_CR9","unstructured":"Gilad-Bachrach, R., Dowlin, N., Laine, K., Lauter, K.E., Naehrig, M., Wernsing, J.: CryptoNets: applying neural networks to encrypted data with high throughput and accuracy. In: Proceedings 33rd International Conference on Machine Learning, pp. 201\u2013210 (2016)"},{"key":"25_CR10","unstructured":"Glorot, X., Bordes, A., Bengio, Y.: Deep sparse rectifier neural networks. In: Proceedings of the 14th International Conference on Artificial Intelligence and Statistics, pp. 315\u2013323 (2011)"},{"key":"25_CR11","unstructured":"Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press, Cambridge, MA (2016). https:\/\/www.deeplearningbook.org"},{"key":"25_CR12","doi-asserted-by":"crossref","unstructured":"Han, K., Hong, S., Cheon, J.H., Park, D.: Logistic regression on homomorphic encrypted data at scale. In: Proceedings of the 33rd AAAI Conference on Artificial Intelligence, pp. 9466\u20139471 (2019)","DOI":"10.1609\/aaai.v33i01.33019466"},{"key":"25_CR13","doi-asserted-by":"crossref","unstructured":"Hartmann, V., Modi, K., Pujol, J.M., West, R.: Privacy-preserving classification with secret vector machines. In: CIKM, pp. 475\u2013484 (2020)","DOI":"10.1145\/3340531.3412051"},{"key":"25_CR14","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"25_CR15","doi-asserted-by":"crossref","unstructured":"Hesamifard, E., Takabi, H., Ghasemi, M.: Deep neural networks classification over encrypted data. In: Proceedings 9th ACM Conference on Data and Application Security Privacy, pp. 97\u2013108 (2019)","DOI":"10.1145\/3292006.3300044"},{"key":"25_CR16","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., van der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2261\u20132269 (2017)","DOI":"10.1109\/CVPR.2017.243"},{"key":"25_CR17","unstructured":"Juvekar, C., Vaikuntanathan, V., Chandrakasan, A.: GAZELLE: a low latency framework for secure neural network inference. In: Proceedings of the 27th USENIX Security Symposium, pp. 1651\u20131669 (2018)"},{"key":"25_CR18","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: Proceedings of the 3rd International Conference on Learning Representations (2015)"},{"key":"25_CR19","unstructured":"Krizhevsky, A., Hinton, G., et al.: Learning multiple layers of features from tiny images. Technical report, University of Toronto (2009)"},{"issue":"11","key":"25_CR20","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y LeCun","year":"1998","unstructured":"LeCun, Y., Bottou, L., Bengio, Y., Haffner, P., et al.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278\u20132324 (1998)","journal-title":"Proc. IEEE"},{"key":"25_CR21","doi-asserted-by":"crossref","unstructured":"Liu, J., Juuti, M., Lu, Y., Asokan, N.: Oblivious neural network predictions via MiniONN transformations. In: Proceedings of the 24th ACM SIGSAC Conference on Computer and Communication Security, pp. 619\u2013631 (2017)","DOI":"10.1145\/3133956.3134056"},{"key":"25_CR22","doi-asserted-by":"crossref","unstructured":"Luo, M.R., Cui, G., Li, C.: Uniform colour spaces based on ciecam02 colour appearance model. Color Res. Appl. 31(4), 320\u2013330 (2006)","DOI":"10.1002\/col.20227"},{"key":"25_CR23","doi-asserted-by":"crossref","unstructured":"Mahendran, A., Vedaldi, A.: Understanding deep image representations by inverting them. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5188\u20135196 (2015)","DOI":"10.1109\/CVPR.2015.7299155"},{"key":"25_CR24","doi-asserted-by":"crossref","unstructured":"Mishra, P., Lehmkuhl, R., Srinivasan, A., Zheng, W., Popa, R.A.: DELPHI: a cryptographic inference service for neural networks. In: Proceedings of 29th USENIX Security Symposium, pp. 2505\u20132522 (2020)","DOI":"10.1145\/3411501.3419418"},{"key":"25_CR25","doi-asserted-by":"crossref","unstructured":"Mohassel, P., Zhang, Y.: SecureML: a system for scalable privacy-preserving machine learning. In: Proceedings of 38th IEEE Symposium on Security Privacy, pp. 19\u201338 (2017)","DOI":"10.1109\/SP.2017.12"},{"key":"25_CR26","doi-asserted-by":"crossref","unstructured":"Naehrig, M., Lauter, K.E., Vaikuntanathan, V.: Can homomorphic encryption be practical? In: Proceedings of the 3rd ACM Cloud Computing Security Workshop, pp. 113\u2013124 (2011)","DOI":"10.1145\/2046660.2046682"},{"key":"25_CR27","unstructured":"Netzer, Y., Wang, T., Coates, A., Bissacco, A., Wu, B., Ng, A.Y.: Reading digits in natural images with unsupervised feature learning. In: Proceedings of the Workshop Deep Learning Unsupervised Feature Learning Neural Information Processing System (2011)"},{"key":"25_CR28","doi-asserted-by":"crossref","unstructured":"Paillier, P.: Public-key cryptosystems based on composite degree residuosity classes. In: Proceedings of the 17th Annual International Conference on Theory Application Cryptographic Techniques, pp. 223\u2013238 (1999)","DOI":"10.1007\/3-540-48910-X_16"},{"key":"25_CR29","doi-asserted-by":"crossref","unstructured":"Popa, R.A., Redfield, C.M.S., Zeldovich, N., Balakrishnan, H.: CryptDB: protecting confidentiality with encrypted query processing. In: Proceedings of the 23rd ACM Symposium on Operating System Principles, pp. 85\u2013100 (2011)","DOI":"10.1145\/2043556.2043566"},{"key":"25_CR30","doi-asserted-by":"crossref","unstructured":"Rathee, D., et al.: CrypTFlow2: practical 2-party secure inference. In: Proceedings of the 27th ACM SIGSAC Conference on Computer and Communications Security, pp. 325\u2013342 (2020)","DOI":"10.1145\/3372297.3417274"},{"key":"25_CR31","unstructured":"Ryffel, T., Pointcheval, D., Bach, F., Dufour-Sans, E., Gay, R.: Partially encrypted deep learning using functional encryption. In: Proceedings of the 33rd Annual Conference on Neural Information Processing System, pp. 4519\u20134530 (2019)"},{"issue":"2","key":"25_CR32","doi-asserted-by":"publisher","first-page":"336","DOI":"10.1007\/s11263-019-01228-7","volume":"128","author":"RR Selvaraju","year":"2020","unstructured":"Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-CAM: visual explanations from deep networks via gradient-based localization. Int. J. Comput. Vis. 128(2), 336\u2013359 (2020)","journal-title":"Int. J. Comput. Vis."},{"key":"25_CR33","unstructured":"Springenberg, J.T., Dosovitskiy, A., Brox, T., Riedmiller, M.A.: Striving for simplicity: the all convolutional net. In: Proceedings of the Workshop 3rd International Conference on Learning Representations (2015)"},{"key":"25_CR34","doi-asserted-by":"publisher","unstructured":"Tsikhanovich, M., Magdon-Ismail, M., Ishaq, M., Zikas, V.: PD-ML-Lite: private distributed machine learning from lightweight cryptography. In: Proceedings of the 22nd Information Security Conference, vol. 11723, pp. 149\u2013167. Springer (2019). https:\/\/doi.org\/10.1007\/978-3-030-30215-3_8","DOI":"10.1007\/978-3-030-30215-3_8"},{"key":"25_CR35","first-page":"442","volume":"2018","author":"S Wagh","year":"2018","unstructured":"Wagh, S., Gupta, D., Chandran, N.: Securenn: Efficient and private neural network training. IACR Cryptology ePrint Archive 2018, 442 (2018)","journal-title":"IACR Cryptology ePrint Archive"},{"issue":"1","key":"25_CR36","first-page":"188","volume":"2021","author":"S Wagh","year":"2021","unstructured":"Wagh, S., Tople, S., Benhamouda, F., Kushilevitz, E., Mittal, P., Rabin, T.: FALCON: honest-majority maliciously secure framework for private deep learning. Proc. Priv. Enhanc. Technol. 2021(1), 188\u2013208 (2021)","journal-title":"Proc. Priv. Enhanc. Technol."},{"key":"25_CR37","unstructured":"Yosinski, J., Clune, J., Nguyen, A.M., Fuchs, T.J., Lipson, H.: Understanding neural networks through deep visualization. arXiv:1506.06579 (2015)"},{"key":"25_CR38","doi-asserted-by":"crossref","unstructured":"Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional networks. In: Proceedings of the European Conference on Computer Vision, pp. 818\u2013833 (2014)","DOI":"10.1007\/978-3-319-10590-1_53"},{"key":"25_CR39","doi-asserted-by":"crossref","unstructured":"Zhang, Q., Wang, C., Wu, H., Xin, C., Phuong, T.V.: GELU-NET: a globally encrypted, locally unencrypted deep neural network for privacy-preserved learning. In: Proceedings of the 27th International Joint Conference on Artificial Intelligence, pp. 3933\u20133939 (2018)","DOI":"10.24963\/ijcai.2018\/547"}],"container-title":["Lecture Notes in Computer Science","Information Security"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-22390-7_25","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,9]],"date-time":"2022-12-09T00:07:58Z","timestamp":1670544478000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-22390-7_25"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031223891","9783031223907"],"references-count":39,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-22390-7_25","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"9 December 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ISC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Information Security","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Bali","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Indonesia","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 December 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 December 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"isw2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/isc2022.petra.ac.id\/","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":"Easy Chair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"72","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":"21","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":"8","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":"29% - 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":"7","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)"}}]}}