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Legal and ethical restrictions on accessing relevant data inhibit data science research in critical domains such as health, finance, and education.
Security and composition of multiparty cryptographic protocols
Ran Canetti · 2000
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General secure multi-party computation from any linear secret-sharing scheme
Ronald Cramer, Ivan Damgård, and Ueli Maurer · 2000
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Calibrating noise to sensitivity in private data analysis
Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith · 2006
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Mechanism design via differential privacy
Frank McSherry and Kunal Talwar · 2007
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Secure computation with fixed-point numbers
O. Catrina and A. Saxena · 2010
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Scikit-learn: Machine learning in Python
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay · 2011
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A simple and practical algorithm for differentially private data release
Moritz Hardt, Katrina Ligett, and Frank McSherry · 2012
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On significance of the least significant bits for differential privacy
Ilya Mironov · 2012
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Secure Multiparty Computation and Secret Sharing
Ronald Cramer, Ivan Damgård, and Jesper Buus Nielsen · 2015
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High-throughput semi-honest secure three-party computation with an honest majority
Toshinori Araki, Jun Furukawa, Yehuda Lindell, Ariel Nof, and Kazuma Ohara · 2016
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Oblivious neural network predictions via miniONN transformations
Jian Liu, Mika Juuti, Yao Lu, and Nadarajah Asokan · 2017
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SecureML: A system for scalable privacy-preserving machine learning
Payman Mohassel and Yupeng Zhang · 2017
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SPD ℤ 2 k \mathbb{Z}_{2^{k}} : Efficient MPC mod 2 k 2^{k} for dishonest majority
Ronald Cramer, Ivan Damgård, Daniel Escudero, Peter Scholl, and Chaoping Xing · 2018
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Privacy-preserving scoring of tree ensembles: A novel framework for AI in healthcare
Kyle Fritchman, Keerthanaa Saminathan, Rafael Dowsley, Tyler Hughes, Martine De Cock, Anderson Nascimento, and Ankur Teredesai · 2018
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Synthea: An approach, method, and software mechanism for generating synthetic patients and the synthetic electronic health care record
Jason Walonoski, Mark Kramer, Joseph Nichols, Andre Quina, Chris Moesel, Dylan Hall, Carlton Duffett, Kudakwashe Dube, Thomas Gallagher, and Scott McLachlan · 2018
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Differentially private generative adversarial network
Liyang Xie, Kaixiang Lin, Shu Wang, Fei Wang, and Jiayu Zhou · 2018
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Protecting privacy of users in brain-computer interface applications
Anisha Agarwal, Rafael Dowsley, Nicholas D. McKinney, Dongrui Wu, Chin-Teng Lin, Martine De Cock, and Anderson C. A. Nascimento · 2019
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Efficient and private scoring of decision trees, support vector machines and logistic regression models based on pre-computation
High performance logistic regression for privacy-preserving genome analysis
Martine De Cock, Rafael Dowsley, Anderson C. A. Nascimento, Davis Railsback, Jianwei Shen, and Ariel Todoki · 2021
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Winning the NIST contest: A scalable and general approach to differentially private synthetic data
Ryan McKenna, Gerome Miklau, and Daniel Sheldon · 2021
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Synthetic data for deep learning , volume 174 of Springer Optimization and its Applications
Sergey I Nikolenko · 2021
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Privacy-preserving video classification with convolutional neural networks
Sikha Pentyala, Rafael Dowsley, and Martine De Cock · 2021
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Privacy-preserving training of tree ensembles over continuous data
Samuel Adams, Chaitali Choudhary, Martine De Cock, Rafael Dowsley, David Melanson, Anderson CA Nascimento, Davis Railsback, and Jianwei Shen · 2022
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Martine De Cock, Rafael Dowsley, Caleb Horst, Raj Katti, Anderson Nascimento, Wing-Sea Poon, and Stacey Truex · 2019
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PATE-GAN: Generating synthetic data with differential privacy guarantees
James Jordon, Jinsung Yoon, and Mihaela Van Der Schaar · 2019
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DP-CGAN: Differentially private synthetic data and label generation
Reihaneh Torkzadehmahani, Peter Kairouz, and Benedict Paten · 2019
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SecureNN: 3-party secure computation for neural network training
Sameer Wagh, Divya Gupta, and Nishanth Chandran · 2019
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MP-SPDZ: A versatile framework for multi-party computation
Marcel Keller · 2020
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Delphi: A cryptographic inference service for neural networks
Pratyush Mishra, Ryan Lehmkuhl, Akshayaram Srinivasan, Wenting Zheng, and Raluca Ada Popa · 2020
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Differentially private synthetic data: Applied evaluations and enhancements
Lucas Rosenblatt, Xiaoyan Liu, Samira Pouyanfar, Eduardo de Leon, Anuj Desai, and Joshua Allen · 2020
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Bangzhou Xin, Wei Yang, Yangyang Geng, Sheng Chen, Shaowei Wang, and Liusheng Huang · 2020
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Fedsyn: Synthetic data generation using federated learning
Monik Raj Behera, Sudhir Upadhyay, Suresh Shetty, Sudha Priyadarshini, Palka Patel, and Ker Farn Lee · 2022
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Distributed, private, sparse histograms in the two-server model
James Bell, Adria Gascon, Badih Ghazi, Ravi Kumar, Pasin Manurangsi, Mariana Raykova, and Phillipp Schoppmann · 2022
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Secure multiparty computations in floating-point arithmetic
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Secure quantized training for deep learning
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Request for information on advancing privacy-enhancing technologies
Science and Technology Policy Office · 2022
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Federated synthetic data generation with differential privacy
Bangzhou Xin, Yangyang Geng, Teng Hu, Sheng Chen, Wei Yang, Shaowei Wang, and Liusheng Huang · 2022
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Gartner identifies top five trends in privacy through 2024
Meghan Rimol · 2024
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