{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T23:08:09Z","timestamp":1784934489717,"version":"3.55.0"},"publisher-location":"New York, NY, USA","reference-count":41,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,5,18]],"date-time":"2021-05-18T00:00:00Z","timestamp":1621296000000},"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":[[2021,5,18]]},"DOI":"10.1145\/3450268.3453519","type":"proceedings-article","created":{"date-parts":[[2021,5,18]],"date-time":"2021-05-18T19:13:35Z","timestamp":1621365215000},"page":"28-39","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":35,"title":["DeepObfuscator"],"prefix":"10.1145","author":[{"given":"Ang","family":"Li","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, Duke University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiayi","family":"Guo","sequence":"additional","affiliation":[{"name":"Department of Automation, Tsinghua University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huanrui","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Duke University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Flora D.","family":"Salim","sequence":"additional","affiliation":[{"name":"Computer Science and Information Technology, School of Science, RMIT University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yiran","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Duke University"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2021,5,18]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"26th {USENIX} Security Symposium ({USENIX} Security 17). 747--764.","author":"Avent Brendan","unstructured":"Brendan Avent , Aleksandra Korolova , David Zeber , Torgeir Hovden , and Benjamin Livshits . 2017. { BLENDER } : Enabling local search with a hybrid differential privacy model . In 26th {USENIX} Security Symposium ({USENIX} Security 17). 747--764. Brendan Avent, Aleksandra Korolova, David Zeber, Torgeir Hovden, and Benjamin Livshits. 2017. {BLENDER }: Enabling local search with a hybrid differential privacy model. In 26th {USENIX} Security Symposium ({USENIX} Security 17). 747--764."},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/2746539.2746632"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D16-1120"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/FOCS.2013.53"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/2660267.2660348"},{"key":"e_1_3_2_1_6_1","volume-title":"Learning anonymized representations with adversarial neural networks. arXiv preprint arXiv:1802.09386","author":"Feutry Cl\u00e9ment","year":"2018","unstructured":"Cl\u00e9ment Feutry , Pablo Piantanida , Yoshua Bengio , and Pierre Duhamel . 2018. Learning anonymized representations with adversarial neural networks. arXiv preprint arXiv:1802.09386 ( 2018 ). Cl\u00e9ment Feutry, Pablo Piantanida, Yoshua Bengio, and Pierre Duhamel. 2018. Learning anonymized representations with adversarial neural networks. arXiv preprint arXiv:1802.09386 (2018)."},{"key":"e_1_3_2_1_7_1","volume-title":"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 International Conference on Machine Learning. 201--210 . 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 International Conference on Machine Learning. 201--210."},{"key":"e_1_3_2_1_8_1","volume-title":"International Conference on Machine Learning. 1737--1746","author":"Gupta Suyog","year":"2015","unstructured":"Suyog Gupta , Ankur Agrawal , Kailash Gopalakrishnan , and Pritish Narayanan . 2015 . Deep learning with limited numerical precision . In International Conference on Machine Learning. 1737--1746 . Suyog Gupta, Ankur Agrawal, Kailash Gopalakrishnan, and Pritish Narayanan. 2015. Deep learning with limited numerical precision. In International Conference on Machine Learning. 1737--1746."},{"key":"e_1_3_2_1_9_1","volume-title":"Huiz iMao, and William J Dally","author":"Han Song","year":"2015","unstructured":"Song Han , Huiz iMao, and William J Dally . 2015 . Deep compression: Compressing de ep neural networks with pruning, trained quantization and huffman coding. arXiv preprint arXiv:1510.00149 (2015). Song Han, Huiz iMao, and William J Dally. 2015. Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding. arXiv preprint arXiv:1510.00149 (2015)."},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.23919\/ACC.2017.7963193"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_2_1_12_1","unstructured":"Kaggle. 2019. State Farm Distracted Driver Detection. https:\/\/www.kaggle.com\/c\/state-farm-distracted-driver-detection.  Kaggle. 2019. State Farm Distracted Driver Detection. https:\/\/www.kaggle.com\/c\/state-farm-distracted-driver-detection."},{"key":"e_1_3_2_1_13_1","volume-title":"Adam: A Method for Stochastic Optimization. arXiv.org (Dec.","author":"Kingma Diederik P","year":"2014","unstructured":"Diederik P Kingma and Jimmy Ba . 2014 . Adam: A Method for Stochastic Optimization. arXiv.org (Dec. 2014), arXiv:1412.6980. arXiv:cs.LG\/1412.6980 Diederik P Kingma and Jimmy Ba. 2014. Adam: A Method for Stochastic Optimization. arXiv.org (Dec. 2014), arXiv:1412.6980. arXiv:cs.LG\/1412.6980"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2009.5459250"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403125"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2007.367856"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/3359303"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.162"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.425"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.184"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7299155"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/3302505.3310068"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46487-9_2"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.165"},{"key":"e_1_3_2_1_25_1","volume-title":"Sina Sajadmanesh, Ali Taheri, Kleomenis Katevas, Hamid R Rabiee, Nicholas D Lane, and Hamed Haddadi.","author":"Osia Seyed Ali","year":"2020","unstructured":"Seyed Ali Osia , Ali Shahin Shamsabadi , Sina Sajadmanesh, Ali Taheri, Kleomenis Katevas, Hamid R Rabiee, Nicholas D Lane, and Hamed Haddadi. 2020 . A hybrid deep learning architecture for privacy-preserving mobile analytics. IEEE Internet of Things Journal ( 2020). Seyed Ali Osia, Ali Shahin Shamsabadi, Sina Sajadmanesh, Ali Taheri, Kleomenis Katevas, Hamid R Rabiee, Nicholas D Lane, and Hamed Haddadi. 2020. A hybrid deep learning architecture for privacy-preserving mobile analytics. IEEE Internet of Things Journal (2020)."},{"key":"e_1_3_2_1_26_1","volume-title":"Ali Taheri, Kleomenis Katevas, Hamid R Rabiee, Nicholas D Lane, and Hamed Haddadi.","author":"Osia Seyed Ali","year":"2017","unstructured":"Seyed Ali Osia , Ali Shahin Shamsabadi , Ali Taheri, Kleomenis Katevas, Hamid R Rabiee, Nicholas D Lane, and Hamed Haddadi. 2017 . Privacy-preserving de ep inference for rich user data on the cloud. arXiv preprint arXiv:1710.01727 (2017). Seyed Ali Osia, Ali Shahin Shamsabadi, Ali Taheri, Kleomenis Katevas, Hamid R Rabiee, Nicholas D Lane, and Hamed Haddadi. 2017. Privacy-preserving deep inference for rich user data on the cloud. arXiv preprint arXiv:1710.01727 (2017)."},{"key":"e_1_3_2_1_27_1","volume-title":"Minos Katevas, Hamed Haddadi, and Hamid RR Rabiee.","author":"Osia Seyed Ali","year":"2018","unstructured":"Seyed Ali Osia , Ali Taheri , Ali Shahin Shamsabadi , Minos Katevas, Hamed Haddadi, and Hamid RR Rabiee. 2018 . Deep private-feature extraction. IEEE Transactions on Knowledge and Data Engineering ( 2018). Seyed Ali Osia, Ali Taheri, Ali Shahin Shamsabadi, Minos Katevas, Hamed Haddadi, and Hamid RR Rabiee. 2018. Deep private-feature extraction. IEEE Transactions on Knowledge and Data Engineering (2018)."},{"key":"e_1_3_2_1_28_1","volume-title":"Scalable private learning with pate. arXiv preprint arXiv:1802.08908","author":"Papernot Nicolas","year":"2018","unstructured":"Nicolas Papernot , Shuang Song , Ilya Mironov , Ananth Raghunathan , Kunal Talwar , and \u00dalfar Erlingsson . 2018. Scalable private learning with pate. arXiv preprint arXiv:1802.08908 ( 2018 ). Nicolas Papernot, Shuang Song, Ilya Mironov, Ananth Raghunathan, Kunal Talwar, and \u00dalfar Erlingsson. 2018. Scalable private learning with pate. arXiv preprint arXiv:1802.08908 (2018)."},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"crossref","unstructured":"Omkar M Parkhi Andrea Vedaldi Andrew Zisserman etal 2015. Deep face recognition.. In bmvc Vol. 1. 6.  Omkar M Parkhi Andrea Vedaldi Andrew Zisserman et al. 2015. Deep face recognition.. In bmvc Vol. 1. 6.","DOI":"10.5244\/C.29.41"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1109\/WACV.2019.00089"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/2976749.2978409"},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"e_1_3_2_1_33_1","volume-title":"Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556","author":"Simonyan Karen","year":"2014","unstructured":"Karen Simonyan and Andrew Zisserman . 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 ( 2014 ). Karen Simonyan and Andrew Zisserman. 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)."},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2017.35"},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1142\/S0218488502001648"},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1145\/3338501.3357370"},{"key":"e_1_3_2_1_37_1","volume-title":"26th {USENIX} Security Symposium ({USENIX} Security 17). 729--745.","author":"Wang Tianhao","unstructured":"Tianhao Wang , Jeremiah Blocki , Ninghui Li , and Somesh Jha . 2017. Locally differentially private protocols for frequency estimation . In 26th {USENIX} Security Symposium ({USENIX} Security 17). 729--745. Tianhao Wang, Jeremiah Blocki, Ninghui Li, and Somesh Jha. 2017. Locally differentially private protocols for frequency estimation. In 26th {USENIX} Security Symposium ({USENIX} Security 17). 729--745."},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACSSC.2003.1292216"},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1109\/AVSS.2014.6918661"},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01270-0_37"},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.225"}],"event":{"name":"IoTDI '21: International Conference on Internet-of-Things Design and Implementation","location":"Charlottesvle VA USA","acronym":"IoTDI '21","sponsor":["SIGBED ACM Special Interest Group on Embedded Systems","IEEE CS"]},"container-title":["Proceedings of the International Conference on Internet-of-Things Design and Implementation"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3450268.3453519","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3450268.3453519","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:46:59Z","timestamp":1750193219000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3450268.3453519"}},"subtitle":["Obfuscating Intermediate Representations with Privacy-Preserving Adversarial Learning on Smartphones"],"short-title":[],"issued":{"date-parts":[[2021,5,18]]},"references-count":41,"alternative-id":["10.1145\/3450268.3453519","10.1145\/3450268"],"URL":"https:\/\/doi.org\/10.1145\/3450268.3453519","relation":{},"subject":[],"published":{"date-parts":[[2021,5,18]]},"assertion":[{"value":"2021-05-18","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}