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Deep learning has been widely applied in many computer vision applications, with remarkable success.
k-anonymity: A model for protecting privacy
Latanya Sweeney. 2002 · 2002
Earlier work this paper cites.
Multiscale structural similarity for image quality assessment. In The Thrity-Seventh Asilomar Conference on Signals, Systems & Computers, 2003 , Vol. 2. Ieee, 1398–1402
Zhou Wang, Eero P Simoncelli, and Alan C Bovik. 2003 · 2003
Earlier work this paper cites.
t-closeness: Privacy beyond k-anonymity and l-diversity. In 2007 IEEE 23rd International Conference on Data Engineering . IEEE, 106–115
Ninghui Li, Tiancheng Li, and Suresh Venkatasubramanian. 2007 · 2007
Earlier work this paper cites.
Attribute and simile classifiers for face verification. In 2009 IEEE 12th International Conference on Computer Vision . IEEE, 365–372
Neeraj Kumar, Alexander C Berg, Peter N Belhumeur, and Shree K Nayar. 2009 · 2009
Earlier work this paper cites.
Local privacy and statistical minimax rates. In 2013 IEEE 54th Annual Symposium on Foundations of Computer Science . IEEE, 429–438
John C Duchi, Michael I Jordan, and Martin J Wainwright. 2013 · 2013
Earlier work this paper cites.
Rappor: Randomized aggregatable privacy-preserving ordinal response. In Proceedings of the 2014 ACM SIGSAC conference on computer and communications security . 1054–1067
Úlfar Erlingsson, Vasyl Pihur, and Aleksandra Korolova. 2014 · 2014
Earlier work this paper cites.
Adam: A Method for Stochastic Optimization
Diederik P Kingma and Jimmy Ba. 2014 · 2014
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman. 2014 · 2014
Earlier work this paper cites.
TrustEYE. M4: protecting the sensor—not the camera. In 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) . IEEE, 159–164
Thomas Winkler, Adám Erdélyi, and Bernhard Rinner. 2014 · 2014
Earlier work this paper cites.
Local, private, efficient protocols for succinct histograms. In Proceedings of the forty-seventh annual ACM symposium on Theory of computing . 127–135
Raef Bassily and Adam Smith. 2015 · 2015
Earlier work this paper cites.
Deep learning with limited numerical precision. In International Conference on Machine Learning . 1737–1746
Suyog Gupta, Ankur Agrawal, Kailash Gopalakrishnan, and Pritish Narayanan. 2015 · 2015
Earlier work this paper cites.
Song Han, Huizi Mao, and William J Dally. 2015 · 2015
Earlier work this paper cites.
Understanding deep image representations by inverting them. In Proceedings of the IEEE conference on computer vision and pattern recognition . 5188–5196
Aravindh Mahendran and Andrea Vedaldi. 2015 · 2015
Earlier work this paper cites.
Deep face recognition.. In bmvc , Vol. 1. 6
Omkar M Parkhi, Andrea Vedaldi, Andrew Zisserman, et al · 2015
Cited alongside, same era.
U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention . Springer, 234–241
Olaf Ronneberger, Philipp Fischer, and Thomas Brox. 2015 · 2015
Cited alongside, same era.
Demographic Dialectal Variation in Social Media: A Case Study of African-American English. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing . Association for Computational Linguistics, Austin, Texas, 1119–1130
Su Lin Blodgett, Lisa Green, and Brendan O’Connor. 2016 · 2016
Cited alongside, same era.
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 · 2016
Cited alongside, same era.
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 · 2017
Later among the works it cites.
Learning anonymized representations with adversarial neural networks
Clément Feutry, Pablo Piantanida, Yoshua Bengio, and Pierre Duhamel. 2018 · 2018
Later among the works it cites.
Deep private-feature extraction
Seyed Ali Osia, Ali Taheri, Ali Shahin Shamsabadi, Minos Katevas, Hamed Haddadi, and Hamid RR Rabiee. 2018 · 2018
Later among the works it cites.
Scalable private learning with pate
Nicolas Papernot, Shuang Song, Ilya Mironov, Ananth Raghunathan, Kunal Talwar, and Úlfar Erlingsson. 2018 · 2018
Later among the works it cites.
Towards privacy-preserving visual recognition via adversarial training: A pilot study. In Proceedings of the European Conference on Computer Vision (ECCV) . 606–624
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Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition . 770–778
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016 · 2016
Cited alongside, same era.
Group mad competition-a new methodology to compare objective image quality models. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . 1664–1673
Kede Ma, Qingbo Wu, Zhou Wang, Zhengfang Duanmu, Hongwei Yong, Hongliang Li, and Lei Zhang. 2016 · 2016
Cited alongside, same era.
Faceless person recognition: Privacy implications in social media. In European Conference on Computer Vision . Springer, 19–35
Seong Joon Oh, Rodrigo Benenson, Mario Fritz, and Bernt Schiele. 2016 · 2016
Cited alongside, same era.
Heavy hitter estimation over set-valued data with local differential privacy. In Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security . 192–203
Zhan Qin, Yin Yang, Ting Yu, Issa Khalil, Xiaokui Xiao, and Kui Ren. 2016 · 2016
Cited alongside, same era.
{ \{ 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 · 2017
Cited alongside, same era.
Differential private noise adding mechanism: Basic conditions and its application. In 2017 American Control Conference (ACC) . IEEE, 1673–1678
Jianping He and Lin Cai. 2017 · 2017
Cited alongside, same era.
Adversarial image perturbation for privacy protection a game theory perspective. In 2017 IEEE International Conference on Computer Vision (ICCV) . IEEE, 1491–1500
Seong Joon Oh, Mario Fritz, and Bernt Schiele. 2017 · 2017
Cited alongside, same era.
Privacy-preserving deep inference for rich user data on the cloud
Seyed Ali Osia, Ali Shahin Shamsabadi, Ali Taheri, Kleomenis Katevas, Hamid R Rabiee, Nicholas D Lane, and Hamed Haddadi. 2017 · 2017
Cited alongside, same era.
Zhenyu Wu, Zhangyang Wang, Zhaowen Wang, and Hailin Jin. 2018 · 2018
Later among the works it cites.
State Farm Distracted Driver Detection
Kaggle. 2019 · 2019
Closest in time.
Privacy Adversarial Network: Representation Learning for Mobile Data Privacy
Sicong Liu, Junzhao Du, Anshumali Shrivastava, and Lin Zhong. 2019 · 2019
Closest in time.
Mobile Sensor Data Anonymization. In Proceedings of the International Conference on Internet of Things Design and Implementation (Montreal, Quebec, Canada) (IoTDI ’19) . Association for Computing Machinery, New York, NY, USA, 49–58
Mohammad Malekzadeh, Richard G. Clegg, Andrea Cavallaro, and Hamed Haddadi. 2019 · 2019
Closest in time.
Learning privacy preserving encodings through adversarial training. In 2019 IEEE Winter Conference on Applications of Computer Vision (WACV) . IEEE, 791–799
Francesco Pittaluga, Sanjeev Koppal, and Ayan Chakrabarti. 2019 · 2019
Closest in time.
A hybrid approach to privacy-preserving federated learning. In Proceedings of the 12th ACM Workshop on Artificial Intelligence and Security . 1–11
Stacey Truex, Nathalie Baracaldo, Ali Anwar, Thomas Steinke, Heiko Ludwig, Rui Zhang, and Yi Zhou. 2019 · 2019
Closest in time.
TIPRDC: task-independent privacy-respecting data crowdsourcing framework for deep learning with anonymized intermediate representations. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining . 824–832
Ang Li, Yixiao Duan, Huanrui Yang, Yiran Chen, and Jianlei Yang. 2020 · 2020
Closest in time.
A hybrid deep learning architecture for privacy-preserving mobile analytics
Seyed Ali Osia, Ali Shahin Shamsabadi, Sina Sajadmanesh, Ali Taheri, Kleomenis Katevas, Hamid R Rabiee, Nicholas D Lane, and Hamed Haddadi. 2020 · 2020
Closest in time.
Ryo Yonetani, Vishnu Naresh Boddeti, Kris M Kitani, and Yoichi Sato. 2017 · 2050
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