{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,12]],"date-time":"2025-11-12T14:22:35Z","timestamp":1762957355978,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":35,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,12,4]],"date-time":"2023-12-04T00:00:00Z","timestamp":1701648000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,12,4]]},"DOI":"10.1145\/3603166.3632132","type":"proceedings-article","created":{"date-parts":[[2024,4,4]],"date-time":"2024-04-04T19:23:27Z","timestamp":1712258607000},"page":"1-10","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["Secure Neural Network Inference as a Service with Resource-Constrained Clients"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-7608-2515","authenticated-orcid":false,"given":"Rik","family":"de Vries","sequence":"first","affiliation":[{"name":"University of Amsterdam, Amsterdam, The Netherlands"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5741-2709","authenticated-orcid":false,"given":"Zolt\u00e1n \u00c1d\u00e1m","family":"Mann","sequence":"additional","affiliation":[{"name":"University of Amsterdam, Amsterdam, The Netherlands"}]}],"member":"320","published-online":{"date-parts":[[2024,4,4]]},"reference":[{"key":"e_1_3_2_1_1_1","article-title":"Review of deep learning: concepts, CNN architectures, challenges, applications, future directions","volume":"8","author":"Alzubaidi Laith","year":"2021","unstructured":"Laith Alzubaidi, Jinglan Zhang, Amjad J. Humaidi, Ayad Al-Dujaili, Ye Duan, Omran Al-Shamma, J. Santamar\u00eda, Mohammed A. Fadhel, Muthana Al-Amidie, and Laith Farhan. 2021. Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. Journal of Big Data 8, 1 (2021).","journal-title":"Journal of Big Data"},{"key":"e_1_3_2_1_2_1","volume-title":"22nd International Symposium on Cluster, Cloud and Internet Computing (CCGrid). IEEE, 279--288","author":"Ayed Dhouha","year":"2022","unstructured":"Dhouha Ayed, Paul-Andrei Dragan, Edith F\u00e9lix, Zolt\u00e1n Ad\u00e1m Mann, Eliot Salant, Robert Seidl, Anestis Sidiropoulos, Steve Taylor, and Ricardo Vitorino. 2022. Protecting sensitive data in the cloud-to-edge continuum: The FogProtect approach. In 22nd International Symposium on Cluster, Cloud and Internet Computing (CCGrid). IEEE, 279--288."},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.3390\/fi15030111"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/1161366.1161393"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1063\/1.1144830"},{"key":"e_1_3_2_1_6_1","volume-title":"On Achieving Privacy-Preserving State-of-the-Art Edge Intelligence. In 4th AAAI Workshop on Privacy-Preserving Artificial Intelligence (PPAI-23)","author":"Chabal Daphnee","year":"2023","unstructured":"Daphnee Chabal, Dolly Sapra, and Zolt\u00e1n \u00c1d\u00e1m Mann. 2023. On Achieving Privacy-Preserving State-of-the-Art Edge Intelligence. In 4th AAAI Workshop on Privacy-Preserving Artificial Intelligence (PPAI-23)."},{"key":"e_1_3_2_1_7_1","volume-title":"33rd International Conference on Machine Learning. 201--210","author":"Gilad-Bachrach Ran","year":"2016","unstructured":"Ran Gilad-Bachrach, Nathan Dowlin, Kim Laine, Kristin Lauter, Michael Naehrig, and John Wernsing. 2016. CryptoNets: Applying neural networks to encrypted data with high throughput and accuracy. In 33rd International Conference on Machine Learning. 201--210."},{"key":"e_1_3_2_1_8_1","unstructured":"Gareth Halfacree. 2020. Raspberry Pi 4 B: How Much RAM Do You Really Need? https:\/\/www.tomshardware.com\/news\/raspberry-pi-4-how-much-ram"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_2_1_10_1","first-page":"4270","article-title":"Model Protection: Real-time privacy-preserving inference service for model privacy at the edge","volume":"19","author":"Hou Jiahui","year":"2021","unstructured":"Jiahui Hou, Huiqi Liu, Yunxin Liu, Yu Wang, Peng-Jun Wan, and Xiang-Yang Li. 2021. Model Protection: Real-time privacy-preserving inference service for model privacy at the edge. IEEE Transactions on Dependable and Secure Computing 19, 6 (2021), 4270--4284.","journal-title":"IEEE Transactions on Dependable and Secure Computing"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.243"},{"key":"e_1_3_2_1_12_1","first-page":"1441","article-title":"A lightweight privacy-preserving CNN feature extraction framework for mobile sensing","volume":"18","author":"Huang Kai","year":"2019","unstructured":"Kai Huang, Ximeng Liu, Shaojing Fu, Deke Guo, and Ming Xu. 2019. A lightweight privacy-preserving CNN feature extraction framework for mobile sensing. IEEE Transactions on Dependable and Secure Computing 18, 3 (2019), 1441--1455.","journal-title":"IEEE Transactions on Dependable and Secure Computing"},{"key":"e_1_3_2_1_13_1","volume-title":"31st USENIX Security Symposium. 809--826","author":"Huang Zhicong","year":"2022","unstructured":"Zhicong Huang, Wen-jie Lu, Cheng Hong, and Jiansheng Ding. 2022. Cheetah: Lean and fast secure two-party deep neural network inference. In 31st USENIX Security Symposium. 809--826."},{"key":"e_1_3_2_1_14_1","unstructured":"Forrest N Iandola Song Han Matthew W Moskewicz Khalid Ashraf William J Dally and Kurt Keutzer. 2016. SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and < 0.5 MB model size. arXiv preprint arXiv:1602.07360."},{"key":"e_1_3_2_1_15_1","volume-title":"27th USENIX Security Symposium (USENIX Security 18)","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 18). 1651--1669."},{"key":"e_1_3_2_1_16_1","volume-title":"IEEE Symposium on Security and Privacy (SP). IEEE, 336--353","author":"Kumar Nishant","year":"2020","unstructured":"Nishant Kumar, Mayank Rathee, Nishanth Chandran, Divya Gupta, Aseem Rastogi, and Rahul Sharma. 2020. CrypTFlow: Secure TensorFlow inference. In IEEE Symposium on Security and Privacy (SP). IEEE, 336--353."},{"key":"e_1_3_2_1_17_1","volume-title":"41st International Conference on Distributed Computing Systems Workshops (ICDCSW). IEEE, 7--12","author":"Lachner Clemens","year":"2021","unstructured":"Clemens Lachner, Zolt\u00e1n Ad\u00e1m Mann, and Schahram Dustdar. 2021. Towards understanding the adaptation space of AI-assisted data protection for video analytics at the edge. In 41st International Conference on Distributed Computing Systems Workshops (ICDCSW). IEEE, 7--12."},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.2307\/249270"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.3390\/app12189010"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/3133956.3134056"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/tdsc.2022.3141391"},{"key":"e_1_3_2_1_22_1","volume-title":"17th International Conference on Service-Oriented Computing (ICSOC). Springer, 283--298","author":"Mann Zolt\u00e1n \u00c1d\u00e1m","year":"2019","unstructured":"Zolt\u00e1n \u00c1d\u00e1m Mann, Andreas Metzger, Johannes Prade, and Robert Seidl. 2019. Optimized application deployment in the fog. In 17th International Conference on Service-Oriented Computing (ICSOC). Springer, 283--298."},{"key":"e_1_3_2_1_23_1","volume-title":"Bos","author":"Mann Zolt\u00e1n \u00c1d\u00e1m","year":"2023","unstructured":"Zolt\u00e1n \u00c1d\u00e1m Mann, Christian Weinert, Daphnee Chabal, and Joppe W. Bos. 2023. Towards Practical Secure Neural Network Inference: The Journey So Far and the Road Ahead. Comput. Surveys (2023), accepted."},{"key":"e_1_3_2_1_24_1","volume-title":"Delphi: A Cryptographic Inference Service for Neural Networks. In USENIX Security Symposium. 2505--2522","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 USENIX Security Symposium. 2505--2522."},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2017.12"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2007\/37343","article-title":"Oblivious neural network computing via homomorphic encryption","volume":"2007","author":"Orlandi Claudio","year":"2007","unstructured":"Claudio Orlandi, Alessandro Piva, and Mauro Barni. 2007. Oblivious neural network computing via homomorphic encryption. EURASIP Journal on Information Security 2007 (2007), 1--11.","journal-title":"EURASIP Journal on Information Security"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2020.2967734"},{"key":"e_1_3_2_1_28_1","volume-title":"IEEE Symposium on Security and Privacy (SP). IEEE, 1003--1020","author":"Rathee Deevashwer","year":"2021","unstructured":"Deevashwer Rathee, Mayank Rathee, Rahul Kranti Kiran Goli, Divya Gupta, Rahul Sharma, Nishanth Chandran, and Aseem Rastogi. 2021. SiRnn: A math library for secure RNN inference. In IEEE Symposium on Security and Privacy (SP). IEEE, 1003--1020."},{"key":"e_1_3_2_1_29_1","volume-title":"Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security (CCS). 325--342","author":"Rathee Deevashwer","year":"2020","unstructured":"Deevashwer Rathee, Mayank Rathee, Nishant Kumar, Nishanth Chandran, Divya Gupta, Aseem Rastogi, and Rahul Sharma. 2020. CrypTFlow2: Practical 2-party secure inference. In Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security (CCS). 325--342."},{"key":"e_1_3_2_1_30_1","volume-title":"28th USENIX Security Symposium. 1501--1518","author":"Riazi M Sadegh","year":"2019","unstructured":"M Sadegh Riazi, Mohammad Samragh, Hao Chen, Kim Laine, Kristin Lauter, and Farinaz Koushanfar. 2019. XONN: XNOR-based oblivious deep neural network inference. In 28th USENIX Security Symposium. 1501--1518."},{"key":"e_1_3_2_1_31_1","volume-title":"Proceedings of the 2018 Asia Conference on Computer and Communications Security. 707--721","author":"Riazi M Sadegh","year":"2018","unstructured":"M Sadegh Riazi, Christian Weinert, Oleksandr Tkachenko, Ebrahim M Songhori, Thomas Schneider, and Farinaz Koushanfar. 2018. Chameleon: A hybrid secure computation framework for machine learning applications. In Proceedings of the 2018 Asia Conference on Computer and Communications Security. 707--721."},{"volume-title":"2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA). IEEE.","author":"Ribeiro Mauro","key":"e_1_3_2_1_32_1","unstructured":"Mauro Ribeiro, Katarina Grolinger, and Miriam A.M. Capretz. 2015. MLaaS: Machine Learning as a Service. In 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA). IEEE."},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1145\/3195970.3196023"},{"volume-title":"The Elements of Big Data Value: Foundations of the Research and Innovation Ecosystem","author":"Timan Tjerk","key":"e_1_3_2_1_34_1","unstructured":"Tjerk Timan and Zoltan Mann. 2021. Data protection in the era of artificial intelligence: trends, existing solutions and recommendations for privacy-preserving technologies. In The Elements of Big Data Value: Foundations of the Research and Innovation Ecosystem. Springer, 153--175."},{"key":"e_1_3_2_1_35_1","volume-title":"1st Intl. Workshop on Challenges in Artificial Intelligence and Machine Learning for Internet of Things. 1--7.","author":"Zheng Mengyao","year":"2019","unstructured":"Mengyao Zheng, Dixing Xu, Linshan Jiang, Chaojie Gu, Rui Tan, and Peng Cheng. 2019. Challenges of privacy-preserving machine learning in IoT. In 1st Intl. Workshop on Challenges in Artificial Intelligence and Machine Learning for Internet of Things. 1--7."}],"event":{"name":"UCC '23: IEEE\/ACM 16th International Conference on Utility and Cloud Computing","sponsor":["SIGARCH ACM Special Interest Group on Computer Architecture","IEEE TCSC"],"location":"Taormina (Messina) Italy","acronym":"UCC '23"},"container-title":["Proceedings of the IEEE\/ACM 16th International Conference on Utility and Cloud Computing"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3603166.3632132","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3603166.3632132","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T22:49:09Z","timestamp":1750286949000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3603166.3632132"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,4]]},"references-count":35,"alternative-id":["10.1145\/3603166.3632132","10.1145\/3603166"],"URL":"https:\/\/doi.org\/10.1145\/3603166.3632132","relation":{},"subject":[],"published":{"date-parts":[[2023,12,4]]},"assertion":[{"value":"2024-04-04","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}