{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:18:10Z","timestamp":1750220290339,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":39,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,4,25]],"date-time":"2022-04-25T00:00:00Z","timestamp":1650844800000},"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":[[2022,4,25]]},"DOI":"10.1145\/3477314.3507128","type":"proceedings-article","created":{"date-parts":[[2022,5,7]],"date-time":"2022-05-07T00:37:36Z","timestamp":1651883856000},"page":"520-529","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Towards privacy aware deep learning for embedded systems"],"prefix":"10.1145","author":[{"given":"Vasisht","family":"Duddu","sequence":"first","affiliation":[{"name":"Univ Lyon, Inria"}]},{"given":"Antoine","family":"Boutet","sequence":"additional","affiliation":[{"name":"Univ Lyon, Inria"}]},{"given":"Virat","family":"Shejwalkar","sequence":"additional","affiliation":[{"name":"University of Massachusetts Amherst"}]}],"member":"320","published-online":{"date-parts":[[2022,5,6]]},"reference":[{"doi-asserted-by":"crossref","unstructured":"Martin Abadi Andy Chu Ian Goodfellow H. Brendan McMahan Ilya Mironov Kunal Talwar and Li Zhang. 2016. Deep Learning with Differential Privacy. In CCS. 308--318.","key":"e_1_3_2_1_1_1","DOI":"10.1145\/2976749.2978318"},{"key":"e_1_3_2_1_2_1","volume-title":"An Analysis of Deep Neural Network Models for Practical Applications. (05","author":"Canziani Alfredo","year":"2016","unstructured":"Alfredo Canziani, Adam Paszke, and Eugenio Culurciello. 2016. An Analysis of Deep Neural Network Models for Practical Applications. (05 2016)."},{"unstructured":"Nicholas Carlini Chang Liu \u00dalfar Erlingsson Jernej Kos and Dawn Song. 2019. The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networks. In USENIX Security. 267--284.","key":"e_1_3_2_1_3_1"},{"doi-asserted-by":"crossref","unstructured":"Andrea Cavallaro Mohammad Malekzadeh and Ali Shahin Shamsabadi. 2020. Deep Learning for Privacy in Multimedia. In MM. 4777--4778.","key":"e_1_3_2_1_4_1","DOI":"10.1145\/3394171.3418551"},{"unstructured":"Jasmine Collins Jascha Sohl-Dickstein and David Sussillo. 2017. Capacity and Trainability in Recurrent Neural Networks. arXiv:1611.09913 [stat.ML]","key":"e_1_3_2_1_5_1"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_6_1","DOI":"10.1109\/JPROC.2020.2976475"},{"doi-asserted-by":"crossref","unstructured":"Vasisht Duddu Antoine Boutet and Virat Shejwalkar. 2020. Quantifying Privacy Leakage in Graph Embedding. In Mobiquitous.","key":"e_1_3_2_1_7_1","DOI":"10.1145\/3448891.3448939"},{"doi-asserted-by":"crossref","unstructured":"Karan Ganju Qi Wang Wei Yang Carl A. Gunter and Nikita Borisov. 2018. Property Inference Attacks on Fully Connected Neural Networks Using Permutation Invariant Representations. In CCS. 619--633.","key":"e_1_3_2_1_8_1","DOI":"10.1145\/3243734.3243834"},{"key":"e_1_3_2_1_9_1","volume-title":"Dally","author":"Han Song","year":"2016","unstructured":"Song Han, Xingyu Liu, Huizi Mao, Jing Pu, Ardavan Pedram, Mark A. Horowitz, and William J. Dally. 2016. EIE: Efficient Inference Engine on Compressed Deep Neural Network. In ISCA. 243--254."},{"key":"e_1_3_2_1_10_1","volume-title":"Deep Compression: Compressing Deep Neural Network with Pruning, Trained Quantization and Huffman Coding. In ICLR.","author":"Han Song","year":"2016","unstructured":"Song Han, Huizi Mao, and WilliamJ. Dally. 2016. Deep Compression: Compressing Deep Neural Network with Pruning, Trained Quantization and Huffman Coding. In ICLR."},{"key":"e_1_3_2_1_11_1","volume-title":"Dally","author":"Han Song","year":"2017","unstructured":"Song Han, Jeff Pool, Sharan Narang, Huizi Mao, Enhao Gong, Shijian Tang, Erich Elsen, Peter Vajda, Manohar Paluri, John Tran, Bryan Catanzaro, and William J. Dally. 2017. DSD: Dense-Sparse-Dense Training for Deep Neural Networks. ICLR (2017)."},{"unstructured":"Song Han Jeff Pool John Tran and WilliamJ. Dally. 2015. Learning Both Weights and Connections for Efficient Neural Networks. In NIPS. 1135--1143.","key":"e_1_3_2_1_12_1"},{"key":"e_1_3_2_1_13_1","first-page":"133","article-title":"LOGAN: Membership Inference Attacks Against Generative Models","volume":"1","author":"Hayes Jamie","year":"2019","unstructured":"Jamie Hayes, Luca Melis, George Danezis, and Emiliano De Cristofaro. 2019. LOGAN: Membership Inference Attacks Against Generative Models. PETS 1 (2019), 133 -- 152.","journal-title":"PETS"},{"key":"e_1_3_2_1_14_1","volume-title":"NIPS Deep Learning and Representation Learning Workshop.","author":"Hinton Geoffrey","year":"2015","unstructured":"Geoffrey Hinton, Oriol Vinyals, and Jeffrey Dean. 2015. Distilling the Knowledge in a Neural Network. In NIPS Deep Learning and Representation Learning Workshop."},{"doi-asserted-by":"crossref","unstructured":"M. Horowitz. 2014. Computing's energy problem (and what we can do about it). In ISSCC. 10--14.","key":"e_1_3_2_1_15_1","DOI":"10.1109\/ISSCC.2014.6757323"},{"unstructured":"Itay Hubara Matthieu Courbariaux Daniel Soudry Ran El-Yaniv and Yoshua Bengio. 2016. Binarized Neural Networks. In NIPS. 4107--4115.","key":"e_1_3_2_1_16_1"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_17_1","DOI":"10.5555\/3122009.3242044"},{"key":"e_1_3_2_1_18_1","volume-title":"SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and &lt;1MB model size. CoRR abs\/1602.07360","author":"Iandola Forrest N.","year":"2016","unstructured":"Forrest N. Iandola, Matthew W. Moskewicz, Khalid Ashraf, Song Han, William J. Dally, and Kurt Keutzer. 2016. SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and &lt;1MB model size. CoRR abs\/1602.07360 (2016)."},{"unstructured":"Jinyuan Jia Ahmed Salem Michael Backes Yang Zhang and Neil Zhenqiang Gong. 2019. MemGuard: Defending against Black-Box Membership Inference Attacks via Adversarial Examples. In CCS. 259--274.","key":"e_1_3_2_1_19_1"},{"unstructured":"Fengfu Li and Bin Liu. 2017. Ternary Weight Networks. In ICLR.","key":"e_1_3_2_1_20_1"},{"key":"e_1_3_2_1_21_1","volume-title":"Emiliano De Cristofaro, and Vitaly Shmatikov","author":"Melis Luca","year":"2019","unstructured":"Luca Melis, Congzheng Song, Emiliano De Cristofaro, and Vitaly Shmatikov. 2019. Exploiting unintended feature leakage in collaborative learning. In SP."},{"doi-asserted-by":"crossref","unstructured":"Milad Nasr Reza Shokri and Amir Houmansadr. 2018. Machine Learning with Membership Privacy using Adversarial Regularization. In CCS. 634--646.","key":"e_1_3_2_1_22_1","DOI":"10.1145\/3243734.3243855"},{"key":"e_1_3_2_1_23_1","volume-title":"Comprehensive Privacy Analysis of Deep Learning: Stand-alone and Federated Learning under Passive and Active White-box Inference Attacks. SP","author":"Nasr Milad","year":"2019","unstructured":"Milad Nasr, Reza Shokri, and Amir Houmansadr. 2019. Comprehensive Privacy Analysis of Deep Learning: Stand-alone and Federated Learning under Passive and Active White-box Inference Attacks. SP (2019)."},{"doi-asserted-by":"crossref","unstructured":"Mohammad Rastegari Vicente Ordonez Joseph Redmon and Ali Farhadi. 2016. XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks. In ECCV.","key":"e_1_3_2_1_24_1","DOI":"10.1007\/978-3-319-46493-0_32"},{"key":"e_1_3_2_1_25_1","volume-title":"XONN: XNOR-based Oblivious Deep Neural Network Inference. In USENIX Security.","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 USENIX Security."},{"volume-title":"White-box vs Black-box: Bayes Optimal Strategies for Membership Inference (PMLR","author":"Sablayrolles Alexandre","unstructured":"Alexandre Sablayrolles, Matthijs Douze, Cordelia Schmid, Yann Ollivier, and Herve Jegou. 2019. White-box vs Black-box: Bayes Optimal Strategies for Membership Inference (PMLR, Vol. 97). 5558--5567.","key":"e_1_3_2_1_26_1"},{"doi-asserted-by":"crossref","unstructured":"Ahmed Salem Yang Zhang Mathias Humbert Mario Fritz and Michael Backes. 2018. ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Models. In NDSS.","key":"e_1_3_2_1_27_1","DOI":"10.14722\/ndss.2019.23119"},{"doi-asserted-by":"crossref","unstructured":"Mark Sandler Andrew G. Howard Menglong Zhu Andrey Zhmoginov and Liang-Chieh Chen. 2018. MobileNetV2: Inverted Residuals and Linear Bottlenecks. In CVPR. 4510--4520.","key":"e_1_3_2_1_28_1","DOI":"10.1109\/CVPR.2018.00474"},{"key":"e_1_3_2_1_29_1","volume-title":"Membership Privacy for Machine Learning Models Through Knowledge Transfer. AAAI","author":"Shejwalkar Virat","year":"2021","unstructured":"Virat Shejwalkar and Amir Houmansadr. 2021. Membership Privacy for Machine Learning Models Through Knowledge Transfer. AAAI (2021)."},{"doi-asserted-by":"crossref","unstructured":"Reza Shokri Marco Stronati Congzheng Song and Vitaly Shmatikov. 2017. Membership inference attacks against machine learning models. In SP.","key":"e_1_3_2_1_30_1","DOI":"10.1109\/SP.2017.41"},{"doi-asserted-by":"crossref","unstructured":"Congzheng Song Thomas Ristenpart and Vitaly Shmatikov. 2017. Machine Learning Models That Remember Too Much. In CCS. 587--601.","key":"e_1_3_2_1_31_1","DOI":"10.1145\/3133956.3134077"},{"key":"e_1_3_2_1_32_1","volume-title":"Systematic Evaluation of Privacy Risks of Machine Learning Models. In arXiv","author":"Song Liwei","year":"2003","unstructured":"Liwei Song and Prateek Mittal. 2020. Systematic Evaluation of Privacy Risks of Machine Learning Models. In arXiv 2003.10595."},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_33_1","DOI":"10.1145\/3319535.3354211"},{"key":"e_1_3_2_1_34_1","volume-title":"Designing Hardware for Machine Learning: The Important Role Played by Circuit Designers","author":"Sze V.","year":"2017","unstructured":"V. Sze. 2017. Designing Hardware for Machine Learning: The Important Role Played by Circuit Designers. IEEE Solid-State Circuits Magazine (2017), 46--54."},{"key":"e_1_3_2_1_35_1","volume-title":"Proc. IEEE","author":"Sze V.","year":"2017","unstructured":"V. Sze, Y. Chen, T. Yang, and J. S. Emer. 2017. Efficient Processing of Deep Neural Networks: A Tutorial and Survey. Proc. IEEE (2017), 2295--2329."},{"doi-asserted-by":"crossref","unstructured":"Wei Tang Gang Hua and Liang Wang. 2017. How to Train a Compact Binary Neural Network with High Accuracy?","key":"e_1_3_2_1_36_1","DOI":"10.1609\/aaai.v31i1.10862"},{"key":"e_1_3_2_1_37_1","volume-title":"Magnus Jahre, and Kees A. Vissers.","author":"Umuroglu Yaman","year":"2017","unstructured":"Yaman Umuroglu, Nicholas J. Fraser, Giulio Gambardella, Michaela Blott, Philip Heng Wai Leong, Magnus Jahre, and Kees A. Vissers. 2017. FINN: A Framework for Fast, Scalable Binarized Neural Network Inference. In FPGA."},{"doi-asserted-by":"crossref","unstructured":"Tien-Ju Yang Yu-Hsin Chen and Vivienne Sze. 2017. Designing Energy-Efficient Convolutional Neural Networks using Energy-Aware Pruning. (2017).","key":"e_1_3_2_1_38_1","DOI":"10.1109\/CVPR.2017.643"},{"key":"e_1_3_2_1_39_1","volume-title":"Machine Learning: Analyzing the Connection to Overfitting. In CSF. 268--282.","author":"Yeom S.","year":"2018","unstructured":"S. Yeom, I. Giacomelli, M. Fredrikson, and S. Jha. 2018. Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting. In CSF. 268--282."}],"event":{"sponsor":["SIGAPP ACM Special Interest Group on Applied Computing"],"acronym":"SAC '22","name":"SAC '22: The 37th ACM\/SIGAPP Symposium on Applied Computing","location":"Virtual Event"},"container-title":["Proceedings of the 37th ACM\/SIGAPP Symposium on Applied Computing"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3477314.3507128","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3477314.3507128","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T19:31:29Z","timestamp":1750188689000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3477314.3507128"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,25]]},"references-count":39,"alternative-id":["10.1145\/3477314.3507128","10.1145\/3477314"],"URL":"https:\/\/doi.org\/10.1145\/3477314.3507128","relation":{},"subject":[],"published":{"date-parts":[[2022,4,25]]},"assertion":[{"value":"2022-05-06","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}