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Applying machine learning (ML) to sensitive domains requires privacy protection of the underlying training data through formal privacy frameworks, such as differential privacy (DP).
MixMatch: A Holistic Approach to Semi-Supervised Learning
David Berthelot, Nicholas Carlini, Ian Goodfellow, Nicolas Papernot, Avital Oliver, and Colin Raffel. 2019 · 1905
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Most people are “privacy pragmatists” who, while concerned about privacy, will sometimes trade it off for other benefits
Humphrey Taylor. 2003 · 2003
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Impacts of user privacy preferences on personalized systems
Maximilian Teltzrow and Alfred Kobsa. 2004 · 2004
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Privacy in e-commerce: Stated preferences vs. actual behavior
Bettina Berendt, Oliver Günther, and Sarah Spiekermann. 2005 · 2005
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Privacy practices of Internet users: Self-reports versus observed behavior
Carlos Jensen, Colin Potts, and Christian Jensen. 2005 · 2005
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Differential privacy. In International Colloquium on Automata, Languages, and Programming . Springer, 1–12
Cynthia Dwork. 2006 · 2006
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Differential privacy: A survey of results. In International conference on theory and applications of models of computation . Springer, 1–19
Cynthia Dwork. 2008 · 2008
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Nudging privacy: The behavioral economics of personal information
Alessandro Acquisti. 2009 · 2009
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Generating user-understandable privacy preferences. In 2009 International Conference on Availability, Reliability and Security . IEEE, 299–306
Jan Kolter and Günther Pernul. 2009 · 2009
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Python 3 Reference Manual
Guido Van Rossum and Fred L. Drake. 2009 · 2009
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MNIST handwritten digit database
Yann LeCun and Corinna Cortes. 2010 · 2010
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Reading digits in natural images with unsupervised feature learning
Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y Ng. 2011 · 2011
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Stochastic gradient descent with differentially private updates. In 2013 IEEE Global Conference on Signal and Information Processing . 245–248
Shuang Song, Kamalika Chaudhuri, and Anand D. Sarwate. 2013 · 2013
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The algorithmic foundations of differential privacy
Cynthia Dwork, Aaron Roth, et al · 2014
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Alan Westin’s privacy homo economicus
Chris Jay Hoofnagle and Jennifer M Urban. 2014 · 2014
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Heterogeneous differential privacy
Mohammad Alaggan, Sébastien Gambs, and Anne-Marie Kermarrec. 2015 · 2015
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Differential privacy: Now it’s getting personal
Hamid Ebadi, David Sands, and Gerardo Schneider. 2015 · 2015
Cited alongside, same era.
Model inversion attacks that exploit confidence information and basic countermeasures. In Proceedings of the 22nd ACM SIGSAC conference on computer and communications security . 1322–1333
Matt Fredrikson, Somesh Jha, and Thomas Ristenpart. 2015 · 2015
Cited alongside, same era.
Conservative or liberal? Personalized differential privacy. In 2015 IEEE 31St international conference on data engineering . IEEE, 1023–1034
Zach Jorgensen, Ting Yu, and Graham Cormode. 2015 · 2015
Cited alongside, same era.
Deep learning with differential privacy. In Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security . 308–318
Martin Abadi, Andy Chu, Ian Goodfellow, H Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang. 2016 · 2016
Cited alongside, same era.
Personalized Differential Privacy Preserving Data Aggregation for Smart Homes. In 3rd International Conference on Wireless Communication and Sensor Networks (WCSN 2016) . Atlantis Press, 203–209
PDP-SAG: Personalized privacy protection in moving objects databases by combining differential privacy and sensitive attribute generalization
Fatemeh Deldar and Mahdi Abadi. 2019 · 2019
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Differentially Private Bagging: Improved utility and cheaper privacy than subsample-and-aggregate. In Advances in Neural Information Processing Systems . pp. 4323–4332
James Jordon, Jinsung Yoon, and Mihaela van der Schaar. 2019 · 2019
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Per-instance Differential Privacy
Yu-Xiang Wang. 2019 · 2019
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Sociocultural Dimensions of Tracking Health and Taking Care
Karthik S Bhat and Neha Kumar. 2020 · 2020
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Utility-aware Exponential Mechanism for Personalized Differential Privacy. In 2020 IEEE Wireless Communications and Networking Conference (WCNC) . IEEE, 1–6
Ben Niu, Yahong Chen, Boyang Wang, Jin Cao, and Fenghua Li. 2020 · 2020
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Xin-Yuan Zhang, Liu-Sheng Huang, Shao-Wei Wang, Zhen-Yu Zhu, and Hong-Li Xu. 2016 · 2016
Cited alongside, same era.
Partitioning-based mechanisms under personalized differential privacy. In Pacific-Asia Conference on Knowledge Discovery and Data Mining . Springer, 615–627
Haoran Li, Li Xiong, Zhanglong Ji, and Xiaoqian Jiang. 2017 · 2017
Cited alongside, same era.
Rényi differential privacy. In 2017 IEEE 30th Computer Security Foundations Symposium (CSF) . IEEE, 263–275
Ilya Mironov. 2017 · 2017
Cited alongside, same era.
Nicolas Papernot, Martín Abadi, Úlfar Erlingsson, Ian Goodfellow, and Kunal Talwar. 2017 · 2017
Cited alongside, same era.
Membership inference attacks against machine learning models. In 2017 IEEE Symposium on Security and Privacy (SP) . IEEE, 3–18
Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov. 2017 · 2017
Cited alongside, same era.
Counterfactual explanations without opening the black box: Automated decisions and the GDPR
Sandra Wachter, Brent Mittelstadt, and Chris Russell. 2017 · 2017
Cited alongside, same era.
Privacy Amplification by Subsampling: Tight Analyses via Couplings and Divergences. In Advances in Neural Information Processing Systems , S. Bengio, H. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, and R. Garnett (Eds.), Vol. 31. Curran Associates, Inc
Borja Balle, Gilles Barthe, and Marco Gaboardi. 2018 · 2018
Cited alongside, same era.
Property inference attacks on fully connected neural networks using permutation invariant representations. In Proceedings of the 2018 ACM SIGSAC conference on computer and communications security . 619–633
Karan Ganju, Qi Wang, Wei Yang, Carl A Gunter, and Nikita Borisov. 2018 · 2018
Cited alongside, same era.
Differentially private learning needs better features (or much more data)
Florian Tramèr and Dan Boneh. 2020 · 2020
Later among the works it cites.
Towards Effective Differential Privacy Communication for Users’ Data Sharing Decision and Comprehension. In 2020 IEEE Symposium on Security and Privacy (SP) . IEEE, 392–410
Aiping Xiong, Tianhao Wang, Ninghui Li, and Somesh Jha. 2020 · 2020
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Private-kNN: Practical Differential Privacy for Computer Vision. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Yuqing Zhu, Xiang Yu, Manmohan Chandraker, and Yu-Xiang Wang. 2020 · 2020
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Personalized privacy preservation for smart grid. In 2021 IEEE International Smart Cities Conference (ISC2) . IEEE, 1–7
Arpan Bhattacharjee, Shahriar Badsha, and Shamik Sengupta. 2021 · 2021
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CaPC Learning: Confidential and Private Collaborative Learning
Christopher A Choquette-Choo, Natalie Dullerud, Adam Dziedzic, Yunxiang Zhang, Somesh Jha, Nicolas Papernot, and Xiao Wang. 2021 · 2021
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Individual privacy accounting via a renyi filter. In Thirty-Fifth Conference on Neural Information Processing Systems
Vitaly Feldman and Tijana Zrnic. 2021 · 2021
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Adversary instantiation: Lower bounds for differentially private machine learning. In 2021 IEEE Symposium on Security and Privacy (SP) . IEEE, 866–882
Milad Nasr, Shuang Songi, Abhradeep Thakurta, Nicolas Papemoti, and Nicholas Carlin. 2021 · 2021
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AdaPDP: Adaptive personalized differential privacy. In IEEE INFOCOM 2021-IEEE Conference on Computer Communications . IEEE, 1–10
Ben Niu, Yahong Chen, Boyang Wang, Zhibo Wang, Fenghua Li, and Jin Cao. 2021 · 2021
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Privacy Needs Reflection: Conceptional Design Rationales for Privacy-Preserving Explanation User Interfaces
Peter Sörries, Claudia Müller-Birn, Katrin Glinka, Franziska Boenisch, Marian Margraf, Sabine Sayegh-Jodehl, and Matthias Rose. 2021 · 2021
Later among the works it cites.
Individual Privacy Accounting for Differentially Private Stochastic Gradient Descent
Da Yu, Gautam Kamath, Janardhan Kulkarni, Tie-Yan Liu, Jian Yin, and Huishuai Zhang. 2022 · 2022
Closest in time.
Adult data set
Ronny Kohavi and Barry Becker. 1996 · 2093
Closest in time.