{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T16:31:10Z","timestamp":1775665870931,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":54,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,10,17]],"date-time":"2022-10-17T00:00:00Z","timestamp":1665964800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"National Natural Science Foundation of China","award":["61972309"],"award-info":[{"award-number":["61972309"]}]},{"name":"Key Technology Innovation Project of Hangzhou","award":["2022AIZD0132"],"award-info":[{"award-number":["2022AIZD0132"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,10,17]]},"DOI":"10.1145\/3511808.3557450","type":"proceedings-article","created":{"date-parts":[[2022,10,16]],"date-time":"2022-10-16T01:22:22Z","timestamp":1665883342000},"page":"1837-1847","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Serpens: Privacy-Preserving Inference through Conditional Separable of Convolutional Neural Networks"],"prefix":"10.1145","author":[{"given":"Longlong","family":"Sun","sequence":"first","affiliation":[{"name":"Xidian University, Xi'an, China"}]},{"given":"Hui","family":"Li","sequence":"additional","affiliation":[{"name":"Xidian University, Xi'an, China"}]},{"given":"Yanguo","family":"Peng","sequence":"additional","affiliation":[{"name":"Xidian University, Xi'an, China"}]},{"given":"Jiangtao","family":"Cui","sequence":"additional","affiliation":[{"name":"Xidian University, Xi'an, China"}]}],"member":"320","published-online":{"date-parts":[[2022,10,17]]},"reference":[{"key":"e_1_3_2_2_1_1","volume-title":"Deep Learning with Differential Privacy. In ACM SIGSAC Conference on Computer and Communications Security, CCS.","author":"Abadi Mart\u00edn","year":"2016","unstructured":"Mart\u00edn Abadi , Andy Chu , Ian J. Goodfellow , H. Brendan McMahan , Ilya Mironov , Kunal Talwar , and Li Zhang . 2016 . Deep Learning with Differential Privacy. In ACM SIGSAC Conference on Computer and Communications Security, CCS. Mart\u00edn Abadi, Andy Chu, Ian J. Goodfellow, H. Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang. 2016. Deep Learning with Differential Privacy. In ACM SIGSAC Conference on Computer and Communications Security, CCS."},{"key":"e_1_3_2_2_2_1","volume-title":"Jamie Ryan Kiros, and Geoffrey E. Hinton","author":"Ba Lei Jimmy","year":"2016","unstructured":"Lei Jimmy Ba , Jamie Ryan Kiros, and Geoffrey E. Hinton . 2016 . Layer Normalization. CoRR abs\/1607.06450 (2016). Lei Jimmy Ba, Jamie Ryan Kiros, and Geoffrey E. Hinton. 2016. Layer Normalization. CoRR abs\/1607.06450 (2016)."},{"key":"e_1_3_2_2_3_1","volume-title":"22nd Annual ACM Symposium on Theory of Computing, STOC.","author":"Beaver Donald","year":"1990","unstructured":"Donald Beaver , Silvio Micali , and Phillip Rogaway . 1990 . The Round Complexity of Secure Protocols (Extended Abstract) . In 22nd Annual ACM Symposium on Theory of Computing, STOC. Donald Beaver, Silvio Micali, and Phillip Rogaway. 1990. The Round Complexity of Secure Protocols (Extended Abstract). In 22nd Annual ACM Symposium on Theory of Computing, STOC."},{"key":"e_1_3_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00942"},{"key":"e_1_3_2_2_5_1","volume-title":"Blake and Vladimir Kolesnikov","author":"Ian","year":"2004","unstructured":"Ian F. Blake and Vladimir Kolesnikov . 2004 . Strong Conditional Oblivious Transfer and Computing on Intervals. In Advances in Cryptology - ASIACRYPT. Ian F. Blake and Vladimir Kolesnikov. 2004. Strong Conditional Oblivious Transfer and Computing on Intervals. In Advances in Cryptology - ASIACRYPT."},{"key":"e_1_3_2_2_6_1","doi-asserted-by":"crossref","unstructured":"Florian Bourse Michele Minelli Matthias Minihold and Pascal Paillier. 2018. Fast Homomorphic Evaluation of Deep Discretized Neural Networks. In Advances in Cryptology - CRYPTO.  Florian Bourse Michele Minelli Matthias Minihold and Pascal Paillier. 2018. Fast Homomorphic Evaluation of Deep Discretized Neural Networks. In Advances in Cryptology - CRYPTO.","DOI":"10.1007\/978-3-319-96878-0_17"},{"key":"e_1_3_2_2_7_1","doi-asserted-by":"crossref","unstructured":"Zvika Brakerski. 2012. Fully Homomorphic Encryption without Modulus Switching from Classical GapSVP. In Advances in Cryptology - CRYPTO.  Zvika Brakerski. 2012. Fully Homomorphic Encryption without Modulus Switching from Classical GapSVP. In Advances in Cryptology - CRYPTO.","DOI":"10.1007\/978-3-642-32009-5_50"},{"key":"e_1_3_2_2_8_1","doi-asserted-by":"crossref","unstructured":"Zvika Brakerski Craig Gentry and Vinod Vaikuntanathan. 2012. (Leveled) fully homomorphic encryption without bootstrapping. In Innovations in Theoretical Computer Science ITCS.  Zvika Brakerski Craig Gentry and Vinod Vaikuntanathan. 2012. (Leveled) fully homomorphic encryption without bootstrapping. In Innovations in Theoretical Computer Science ITCS.","DOI":"10.1145\/2090236.2090262"},{"key":"e_1_3_2_2_9_1","doi-asserted-by":"publisher","DOI":"10.14722\/ndss.2020.23005"},{"key":"e_1_3_2_2_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/3319535.3363207"},{"key":"e_1_3_2_2_11_1","doi-asserted-by":"crossref","unstructured":"Jung Hee Cheon Kyoohyung Han Andrey Kim Miran Kim and Yongsoo Song. 2018. A Full RNS Variant of Approximate Homomorphic Encryption. In Selected Areas in Cryptography - SAC.  Jung Hee Cheon Kyoohyung Han Andrey Kim Miran Kim and Yongsoo Song. 2018. A Full RNS Variant of Approximate Homomorphic Encryption. In Selected Areas in Cryptography - SAC.","DOI":"10.1007\/978-3-030-10970-7_16"},{"key":"e_1_3_2_2_12_1","doi-asserted-by":"crossref","unstructured":"Jung Hee Cheon Andrey Kim Miran Kim and Yong Soo Song. 2017. Homomorphic Encryption for Arithmetic of Approximate Numbers. In Advances in Cryptology - ASIACRYPT.  Jung Hee Cheon Andrey Kim Miran Kim and Yong Soo Song. 2017. Homomorphic Encryption for Arithmetic of Approximate Numbers. In Advances in Cryptology - ASIACRYPT.","DOI":"10.1007\/978-3-319-70694-8_15"},{"key":"e_1_3_2_2_13_1","volume-title":"Selective Network Linearization for Efficient Private Inference. In 39th International Conference on Machine Learning, ICML.","author":"Cho Minsu","year":"2022","unstructured":"Minsu Cho , Ameya Joshi , Brandon Reagen , Siddharth Garg , and Chinmay Hegde . 2022 . Selective Network Linearization for Efficient Private Inference. In 39th International Conference on Machine Learning, ICML. Minsu Cho, Ameya Joshi, Brandon Reagen, Siddharth Garg, and Chinmay Hegde. 2022. Selective Network Linearization for Efficient Private Inference. In 39th International Conference on Machine Learning, ICML."},{"key":"e_1_3_2_2_14_1","volume-title":"Faster CryptoNets: Leveraging Sparsity for Real-World Encrypted Inference. CoRR abs\/1811.09953","author":"Chou Edward","year":"2018","unstructured":"Edward Chou , Josh Beal , Daniel Levy , Serena Yeung , Albert Haque , and Li Fei-Fei . 2018. Faster CryptoNets: Leveraging Sparsity for Real-World Encrypted Inference. CoRR abs\/1811.09953 ( 2018 ). Edward Chou, Josh Beal, Daniel Levy, Serena Yeung, Albert Haque, and Li Fei-Fei. 2018. Faster CryptoNets: Leveraging Sparsity for Real-World Encrypted Inference. CoRR abs\/1811.09953 (2018)."},{"key":"e_1_3_2_2_15_1","volume-title":"Privacy-preserving Neural Representations of Text. In Conference on Empirical Methods in Natural Language Processing, EMNLP.","author":"Coavoux Maximin","unstructured":"Maximin Coavoux , Shashi Narayan , and Shay B. Cohen . 2018 . Privacy-preserving Neural Representations of Text. In Conference on Empirical Methods in Natural Language Processing, EMNLP. Maximin Coavoux, Shashi Narayan, and Shay B. Cohen. 2018. Privacy-preserving Neural Representations of Text. In Conference on Empirical Methods in Natural Language Processing, EMNLP."},{"key":"e_1_3_2_2_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/TDSC.2017.2679189"},{"key":"e_1_3_2_2_17_1","volume-title":"ABY - A Framework for Efficient Mixed-Protocol Secure Two-Party Computation. In 22nd Annual Network and Distributed System Security Symposium, NDSS.","author":"Demmler Daniel","year":"2015","unstructured":"Daniel Demmler , Thomas Schneider , and Michael Zohner . 2015 . ABY - A Framework for Efficient Mixed-Protocol Secure Two-Party Computation. In 22nd Annual Network and Distributed System Security Symposium, NDSS. Daniel Demmler, Thomas Schneider, and Michael Zohner. 2015. ABY - A Framework for Efficient Mixed-Protocol Secure Two-Party Computation. In 22nd Annual Network and Distributed System Security Symposium, NDSS."},{"key":"e_1_3_2_2_18_1","volume-title":"Towards Plausible Differentially Private ADMM Based Distributed Machine Learning. In 29th ACM International Conference on Information and Knowledge Management, CIKM.","author":"Ding Jiahao","year":"2020","unstructured":"Jiahao Ding , JingyiWang, Guannan Liang , Jinbo Bi , and Miao Pan . 2020 . Towards Plausible Differentially Private ADMM Based Distributed Machine Learning. In 29th ACM International Conference on Information and Knowledge Management, CIKM. Jiahao Ding, JingyiWang, Guannan Liang, Jinbo Bi, and Miao Pan. 2020. Towards Plausible Differentially Private ADMM Based Distributed Machine Learning. In 29th ACM International Conference on Information and Knowledge Management, CIKM."},{"key":"e_1_3_2_2_19_1","first-page":"144","article-title":"Somewhat Practical Fully Homomorphic Encryption","volume":"2012","author":"Fan Junfeng","year":"2012","unstructured":"Junfeng Fan and Frederik Vercauteren . 2012 . Somewhat Practical Fully Homomorphic Encryption . IACR Cryptol. ePrint Arch. 2012 (2012), 144 . Junfeng Fan and Frederik Vercauteren. 2012. Somewhat Practical Fully Homomorphic Encryption. IACR Cryptol. ePrint Arch. 2012 (2012), 144.","journal-title":"IACR Cryptol. ePrint Arch."},{"key":"e_1_3_2_2_21_1","volume-title":"Smart","author":"Gentry Craig","year":"2012","unstructured":"Craig Gentry , Shai Halevi , and Nigel P . Smart . 2012 . Fully Homomorphic Encryption with Polylog Overhead. In Advances in Cryptology - EUROCRYPT. Craig Gentry, Shai Halevi, and Nigel P. Smart. 2012. Fully Homomorphic Encryption with Polylog Overhead. In Advances in Cryptology - EUROCRYPT."},{"key":"e_1_3_2_2_22_1","volume-title":"Annual Conference on Neural Information Processing Systems, NeurIPS.","author":"Ghodsi Zahra","year":"2020","unstructured":"Zahra Ghodsi , Akshaj Kumar Veldanda , Brandon Reagen , and Siddharth Garg . 2020 . CryptoNAS: Private Inference on a ReLU Budget . In Annual Conference on Neural Information Processing Systems, NeurIPS. Zahra Ghodsi, Akshaj Kumar Veldanda, Brandon Reagen, and Siddharth Garg. 2020. CryptoNAS: Private Inference on a ReLU Budget. In Annual Conference on Neural Information Processing Systems, NeurIPS."},{"key":"e_1_3_2_2_23_1","volume-title":"33nd International Conference on Machine Learning, ICML.","author":"Gilad-Bachrach Ran","year":"2016","unstructured":"Ran Gilad-Bachrach , Nathan Dowlin , Kim Laine , Kristin E. Lauter , Michael Naehrig , and John Wernsing . 2016 . CryptoNets: Applying Neural Networks to Encrypted Data with High Throughput and Accuracy . In 33nd International Conference on Machine Learning, ICML. Ran Gilad-Bachrach, Nathan Dowlin, Kim Laine, Kristin E. Lauter, Michael Naehrig, and John Wernsing. 2016. CryptoNets: Applying Neural Networks to Encrypted Data with High Throughput and Accuracy. In 33nd International Conference on Machine Learning, ICML."},{"key":"e_1_3_2_2_24_1","volume-title":"19th Annual ACM Symposium on Theory of Computing, STOC.","author":"Goldreich Oded","year":"1987","unstructured":"Oded Goldreich , Silvio Micali , and Avi Wigderson . 1987 . How to Play any Mental Game or A Completeness Theorem for Protocols with Honest Majority . In 19th Annual ACM Symposium on Theory of Computing, STOC. Oded Goldreich, Silvio Micali, and Avi Wigderson. 1987. How to Play any Mental Game or A Completeness Theorem for Protocols with Honest Majority. In 19th Annual ACM Symposium on Theory of Computing, STOC."},{"key":"e_1_3_2_2_25_1","volume-title":"Deep Residual Learning for Image Recognition. In IEEE Conference on Computer Vision and Pattern Recognition, CVPR.","author":"He Kaiming","year":"2016","unstructured":"Kaiming He , Xiangyu Zhang , Shaoqing Ren , and Jian Sun . 2016 . Deep Residual Learning for Image Recognition. In IEEE Conference on Computer Vision and Pattern Recognition, CVPR. Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep Residual Learning for Image Recognition. In IEEE Conference on Computer Vision and Pattern Recognition, CVPR."},{"key":"e_1_3_2_2_26_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00140"},{"key":"e_1_3_2_2_27_1","volume-title":"Densely Connected Convolutional Networks. In IEEE Conference on Computer Vision and Pattern Recognition, CVPR.","author":"Huang Gao","unstructured":"Gao Huang , Zhuang Liu , Laurens van der Maaten, and Kilian Q.Weinberger. 2017 . Densely Connected Convolutional Networks. In IEEE Conference on Computer Vision and Pattern Recognition, CVPR. Gao Huang, Zhuang Liu, Laurens van der Maaten, and Kilian Q.Weinberger. 2017. Densely Connected Convolutional Networks. In IEEE Conference on Computer Vision and Pattern Recognition, CVPR."},{"key":"e_1_3_2_2_28_1","doi-asserted-by":"publisher","DOI":"10.1109\/TDSC.2017.2751476"},{"key":"e_1_3_2_2_29_1","volume-title":"GAZELLE: A Low Latency Framework for Secure Neural Network Inference. In 27th USENIX Security Symposium, USENIX Security.","author":"Juvekar Chiraag","year":"2018","unstructured":"Chiraag Juvekar , Vinod Vaikuntanathan , and Anantha Chandrakasan . 2018 . GAZELLE: A Low Latency Framework for Secure Neural Network Inference. In 27th USENIX Security Symposium, USENIX Security. Chiraag Juvekar, Vinod Vaikuntanathan, and Anantha Chandrakasan. 2018. GAZELLE: A Low Latency Framework for Secure Neural Network Inference. In 27th USENIX Security Symposium, USENIX Security."},{"key":"e_1_3_2_2_30_1","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"e_1_3_2_2_31_1","doi-asserted-by":"publisher","DOI":"10.1007\/11496137_31"},{"key":"e_1_3_2_2_32_1","doi-asserted-by":"crossref","unstructured":"Yehuda Lindell. 2017. How to Simulate It - A Tutorial on the Simulation Proof Technique. In Tutorials on the Foundations of Cryptography. 277--346.  Yehuda Lindell. 2017. How to Simulate It - A Tutorial on the Simulation Proof Technique. In Tutorials on the Foundations of Cryptography. 277--346.","DOI":"10.1007\/978-3-319-57048-8_6"},{"key":"e_1_3_2_2_33_1","volume-title":"ACM SIGSAC Conference on Computer and Communications Security, CCS.","author":"Liu Jian","unstructured":"Jian Liu , Mika Juuti , Yao Lu , and N. Asokan . 2017. Oblivious Neural Network Predictions via MiniONN Transformations . In ACM SIGSAC Conference on Computer and Communications Security, CCS. Jian Liu, Mika Juuti, Yao Lu, and N. Asokan. 2017. Oblivious Neural Network Predictions via MiniONN Transformations. In ACM SIGSAC Conference on Computer and Communications Security, CCS."},{"key":"e_1_3_2_2_34_1","volume-title":"HEMET: A Homomorphic-Encryption-Friendly Privacy-Preserving Mobile Neural Network Architecture. In 38th International Conference on Machine Learning, ICML.","author":"Lou Qian","year":"2021","unstructured":"Qian Lou and Lei Jiang . 2021 . HEMET: A Homomorphic-Encryption-Friendly Privacy-Preserving Mobile Neural Network Architecture. In 38th International Conference on Machine Learning, ICML. Qian Lou and Lei Jiang. 2021. HEMET: A Homomorphic-Encryption-Friendly Privacy-Preserving Mobile Neural Network Architecture. In 38th International Conference on Machine Learning, ICML."},{"key":"e_1_3_2_2_35_1","volume-title":"Delphi: A Cryptographic Inference Service for Neural Networks. In 29th USENIX Security Symposium, USENIX Security.","author":"Mishra Pratyush","year":"2020","unstructured":"Pratyush Mishra , Ryan Lehmkuhl , Akshayaram Srinivasan , Wenting Zheng , and Raluca Ada Popa . 2020 . Delphi: A Cryptographic Inference Service for Neural Networks. In 29th USENIX Security Symposium, USENIX Security. Pratyush Mishra, Ryan Lehmkuhl, Akshayaram Srinivasan, Wenting Zheng, and Raluca Ada Popa. 2020. Delphi: A Cryptographic Inference Service for Neural Networks. In 29th USENIX Security Symposium, USENIX Security."},{"key":"e_1_3_2_2_36_1","volume-title":"ACM SIGSAC Conference on Computer and Communications Security, CCS.","author":"Mohassel Payman","year":"2018","unstructured":"Payman Mohassel and Peter Rindal . 2018 . ABY3: A Mixed Protocol Framework for Machine Learning . In ACM SIGSAC Conference on Computer and Communications Security, CCS. Payman Mohassel and Peter Rindal. 2018. ABY3: A Mixed Protocol Framework for Machine Learning. In ACM SIGSAC Conference on Computer and Communications Security, CCS."},{"key":"e_1_3_2_2_37_1","volume-title":"SecureML: A System for Scalable Privacy-Preserving Machine Learning. In IEEE Symposium on Security and Privacy, SP.","author":"Mohassel Payman","year":"2017","unstructured":"Payman Mohassel and Yupeng Zhang . 2017 . SecureML: A System for Scalable Privacy-Preserving Machine Learning. In IEEE Symposium on Security and Privacy, SP. Payman Mohassel and Yupeng Zhang. 2017. SecureML: A System for Scalable Privacy-Preserving Machine Learning. In IEEE Symposium on Security and Privacy, SP."},{"key":"e_1_3_2_2_38_1","volume-title":"Adversarial Image Perturbation for Privacy Protection A Game Theory Perspective. In IEEE International Conference on Computer Vision, ICCV.","author":"Oh Seong Joon","year":"2017","unstructured":"Seong Joon Oh , Mario Fritz , and Bernt Schiele . 2017 . Adversarial Image Perturbation for Privacy Protection A Game Theory Perspective. In IEEE International Conference on Computer Vision, ICCV. Seong Joon Oh, Mario Fritz, and Bernt Schiele. 2017. Adversarial Image Perturbation for Privacy Protection A Game Theory Perspective. In IEEE International Conference on Computer Vision, ICCV."},{"key":"e_1_3_2_2_39_1","volume-title":"5th International Conference on Learning Representations, ICLR.","author":"Papernot Nicolas","year":"2017","unstructured":"Nicolas Papernot , Mart\u00edn Abadi , \u00dalfar Erlingsson , Ian J. Goodfellow , and Kunal Talwar . 2017 . Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data . In 5th International Conference on Learning Representations, ICLR. Nicolas Papernot, Mart\u00edn Abadi, \u00dalfar Erlingsson, Ian J. Goodfellow, and Kunal Talwar. 2017. Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data. In 5th International Conference on Learning Representations, ICLR."},{"key":"e_1_3_2_2_40_1","volume-title":"30th USENIX Security Symposium, USENIX Security.","author":"Patra Arpita","year":"2021","unstructured":"Arpita Patra , Thomas Schneider , Ajith Suresh , and Hossein Yalame . 2021 . ABY2.0: Improved Mixed-Protocol Secure Two-Party Computation . In 30th USENIX Security Symposium, USENIX Security. Arpita Patra, Thomas Schneider, Ajith Suresh, and Hossein Yalame. 2021. ABY2.0: Improved Mixed-Protocol Secure Two-Party Computation. In 30th USENIX Security Symposium, USENIX Security."},{"key":"e_1_3_2_2_41_1","volume-title":"Annual Conference on Neural Information Processing Systems, NeurIPS.","author":"Ryffel Theo","year":"2019","unstructured":"Theo Ryffel , David Pointcheval , Francis R. Bach , Edouard Dufour-Sans , and Romain Gay . 2019 . Partially Encrypted Deep Learning using Functional Encryption . In Annual Conference on Neural Information Processing Systems, NeurIPS. Theo Ryffel, David Pointcheval, Francis R. Bach, Edouard Dufour-Sans, and Romain Gay. 2019. Partially Encrypted Deep Learning using Functional Encryption. In Annual Conference on Neural Information Processing Systems, NeurIPS."},{"key":"e_1_3_2_2_42_1","volume-title":"35th International Conference on Machine Learning, ICML.","author":"Sanyal Amartya","year":"2018","unstructured":"Amartya Sanyal , Matt J. Kusner , Adri\u00e0 Gasc\u00f3n , and Varun Kanade . 2018 . TAPAS: Tricks to Accelerate (encrypted) Prediction As a Service . In 35th International Conference on Machine Learning, ICML. Amartya Sanyal, Matt J. Kusner, Adri\u00e0 Gasc\u00f3n, and Varun Kanade. 2018. TAPAS: Tricks to Accelerate (encrypted) Prediction As a Service. In 35th International Conference on Machine Learning, ICML."},{"key":"e_1_3_2_2_43_1","volume-title":"https:\/\/github.com\/Microsoft\/SEAL. Microsoft Research","author":"SEAL","unstructured":"SEAL [n.d.]. Microsoft SEAL. https:\/\/github.com\/Microsoft\/SEAL. Microsoft Research , Redmond, WA . SEAL [n.d.]. Microsoft SEAL. https:\/\/github.com\/Microsoft\/SEAL. Microsoft Research, Redmond, WA."},{"key":"e_1_3_2_2_44_1","first-page":"133","article-title":"Fully Homomorphic SIMD Operations","volume":"2011","author":"Smart Nigel P.","year":"2011","unstructured":"Nigel P. Smart and Frederik Vercauteren . 2011 . Fully Homomorphic SIMD Operations . IACR Cryptol. ePrint Arch. 2011 (2011), 133 . Nigel P. Smart and Frederik Vercauteren. 2011. Fully Homomorphic SIMD Operations. IACR Cryptol. ePrint Arch. 2011 (2011), 133.","journal-title":"IACR Cryptol. ePrint Arch."},{"key":"e_1_3_2_2_45_1","volume-title":"Verifiable and Private Execution of Neural Networks in Trusted Hardware. In 7th International Conference on Learning Representations, ICLR.","author":"Tram\u00e8r Florian","year":"2019","unstructured":"Florian Tram\u00e8r and Dan Boneh . 2019 . Slalom: Fast , Verifiable and Private Execution of Neural Networks in Trusted Hardware. In 7th International Conference on Learning Representations, ICLR. Florian Tram\u00e8r and Dan Boneh. 2019. Slalom: Fast, Verifiable and Private Execution of Neural Networks in Trusted Hardware. In 7th International Conference on Learning Representations, ICLR."},{"key":"e_1_3_2_2_46_1","volume-title":"Lempitsky","author":"Ulyanov Dmitry","year":"2016","unstructured":"Dmitry Ulyanov , Andrea Vedaldi , and Victor S . Lempitsky . 2016 . Instance Normalization : The Missing Ingredient for Fast Stylization. CoRR abs\/1607.08022 (2016). Dmitry Ulyanov, Andrea Vedaldi, and Victor S. Lempitsky. 2016. Instance Normalization: The Missing Ingredient for Fast Stylization. CoRR abs\/1607.08022 (2016)."},{"key":"e_1_3_2_2_47_1","first-page":"442","article-title":"SecureNN: Efficient and Private Neural Network Training","volume":"2018","author":"Wagh Sameer","year":"2018","unstructured":"Sameer Wagh , Divya Gupta , and Nishanth Chandran . 2018 . SecureNN: Efficient and Private Neural Network Training . IACR Cryptol. ePrint Arch. 2018 (2018), 442 . Sameer Wagh, Divya Gupta, and Nishanth Chandran. 2018. SecureNN: Efficient and Private Neural Network Training. IACR Cryptol. ePrint Arch. 2018 (2018), 442.","journal-title":"IACR Cryptol. ePrint Arch."},{"key":"e_1_3_2_2_48_1","volume-title":"Group Normalization. In 15th European Conference on Computer Vision, ECCV.","author":"Wu Yuxin","year":"2018","unstructured":"Yuxin Wu and Kaiming He . 2018 . Group Normalization. In 15th European Conference on Computer Vision, ECCV. Yuxin Wu and Kaiming He. 2018. Group Normalization. In 15th European Conference on Computer Vision, ECCV."},{"key":"e_1_3_2_2_49_1","volume-title":"Interpretable Complex-Valued Neural Networks for Privacy Protection. In 8th International Conference on Learning Representations, ICLR.","author":"Xiang Liyao","year":"2020","unstructured":"Liyao Xiang , Hao Zhang , Haotian Ma , Yifan Zhang , Jie Ren , and Quanshi Zhang . 2020 . Interpretable Complex-Valued Neural Networks for Privacy Protection. In 8th International Conference on Learning Representations, ICLR. Liyao Xiang, Hao Zhang, Haotian Ma, Yifan Zhang, Jie Ren, and Quanshi Zhang. 2020. Interpretable Complex-Valued Neural Networks for Privacy Protection. In 8th International Conference on Learning Representations, ICLR."},{"key":"e_1_3_2_2_50_1","volume-title":"Adversarial Learning of Privacy-Preserving and Task-Oriented Representations. In The 34th AAAI Conference on Artificial Intelligence, AAAI.","author":"Xiao Taihong","year":"2020","unstructured":"Taihong Xiao , Yi-Hsuan Tsai , Kihyuk Sohn , Manmohan Chandraker , and Ming-Hsuan Yang . 2020 . Adversarial Learning of Privacy-Preserving and Task-Oriented Representations. In The 34th AAAI Conference on Artificial Intelligence, AAAI. Taihong Xiao, Yi-Hsuan Tsai, Kihyuk Sohn, Manmohan Chandraker, and Ming-Hsuan Yang. 2020. Adversarial Learning of Privacy-Preserving and Task-Oriented Representations. In The 34th AAAI Conference on Artificial Intelligence, AAAI."},{"key":"e_1_3_2_2_51_1","doi-asserted-by":"publisher","DOI":"10.1145\/3298981"},{"key":"e_1_3_2_2_52_1","volume-title":"23rd Annual Symposium on Foundations of Computer Science, FOCS.","author":"Chi-Chih Yao Andrew","year":"1982","unstructured":"Andrew Chi-Chih Yao . 1982 . Protocols for Secure Computations (Extended Abstract) . In 23rd Annual Symposium on Foundations of Computer Science, FOCS. Andrew Chi-Chih Yao. 1982. Protocols for Secure Computations (Extended Abstract). In 23rd Annual Symposium on Foundations of Computer Science, FOCS."},{"key":"e_1_3_2_2_53_1","volume-title":"Differentially Private Model Publishing for Deep Learning. In IEEE Symposium on Security and Privacy, SP.","author":"Yu Lei","year":"2019","unstructured":"Lei Yu , Ling Liu , Calton Pu , Mehmet Emre Gursoy , and Stacey Truex . 2019 . Differentially Private Model Publishing for Deep Learning. In IEEE Symposium on Security and Privacy, SP. Lei Yu, Ling Liu, Calton Pu, Mehmet Emre Gursoy, and Stacey Truex. 2019. Differentially Private Model Publishing for Deep Learning. In IEEE Symposium on Security and Privacy, SP."},{"key":"e_1_3_2_2_54_1","volume-title":"ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices. In IEEE Conference on Computer Vision and Pattern Recognition, CVPR.","author":"Zhang Xiangyu","year":"2018","unstructured":"Xiangyu Zhang , Xinyu Zhou , Mengxiao Lin , and Jian Sun . 2018 . ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices. In IEEE Conference on Computer Vision and Pattern Recognition, CVPR. Xiangyu Zhang, Xinyu Zhou, Mengxiao Lin, and Jian Sun. 2018. ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices. In IEEE Conference on Computer Vision and Pattern Recognition, CVPR."},{"key":"e_1_3_2_2_55_1","volume-title":"PrivColl: Practical Privacy-Preserving Collaborative Machine Learning. In 25th European Symposium on Research in Computer Security, ESORICS.","author":"Zhang Yanjun","unstructured":"Yanjun Zhang , Guangdong Bai , Xue Li , Caitlin Curtis , Chen Chen , and Ryan K. L. Ko . 2020 . PrivColl: Practical Privacy-Preserving Collaborative Machine Learning. In 25th European Symposium on Research in Computer Security, ESORICS. Yanjun Zhang, Guangdong Bai, Xue Li, Caitlin Curtis, Chen Chen, and Ryan K. L. Ko. 2020. PrivColl: Practical Privacy-Preserving Collaborative Machine Learning. In 25th European Symposium on Research in Computer Security, ESORICS."}],"event":{"name":"CIKM '22: The 31st ACM International Conference on Information and Knowledge Management","location":"Atlanta GA USA","acronym":"CIKM '22","sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web","SIGIR ACM Special Interest Group on Information Retrieval"]},"container-title":["Proceedings of the 31st ACM International Conference on Information & Knowledge Management"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3511808.3557450","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3511808.3557450","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T17:48:55Z","timestamp":1750182535000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3511808.3557450"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,17]]},"references-count":54,"alternative-id":["10.1145\/3511808.3557450","10.1145\/3511808"],"URL":"https:\/\/doi.org\/10.1145\/3511808.3557450","relation":{},"subject":[],"published":{"date-parts":[[2022,10,17]]},"assertion":[{"value":"2022-10-17","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}