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Differential privacy (DP) ensures that training a machine learning model does not leak private data.
On measures of entropy and information
Rényi, A · 1961
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Gradient methods for the minimisation of functionals
Polyak, B. T · 1963
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Problem complexity and method efficiency in optimization
Nemirovski, A. and Yudin, D · 1983
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Assouad, fano, and le cam
Yu, B · 1997
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The enron corpus: A new dataset for email classification research
Klimt, B. and Yang, Y · 2004
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Near instance-optimality in differential privacy
Asi, H. and Duchi, J. C · 2005
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The personal genome project, 2005
Church, G. M · 2005
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Calibrating noise to sensitivity in private data analysis
Dwork, C., McSherry, F., Nissim, K., and Smith, A · 2006
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Differentially private language models benefit from public pre-training
Kerrigan, G., Slack, D., and Tuyls, J · 2009
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Differentially private language models benefit from public pre-training
Kerrigan, G., Slack, D., and Tuyls, J · 2009
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Learning multiple layers of features from tiny images
Krizhevsky, A., Hinton, G., et al · 2009
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Privacy integrated queries: an extensible platform for privacy-preserving data analysis
McSherry, F. D · 2009
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On the geometry of differential privacy
Hardt, M. and Talwar, K · 2010
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Differentially private empirical risk minimization
Chaudhuri, K., Monteleoni, C., and Sarwate, A. D · 2011
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What can we learn privately?
Kasiviswanathan, S. P., Lee, H. K., Nissim, K., Raskhodnikova, S., and Smith, A · 2011
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Optimal stochastic approximation algorithms for strongly convex stochastic composite optimization i: A generic algorithmic framework
Ghadimi, S. and Lan, G · 2012
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Making gradient descent optimal for strongly convex stochastic optimization
Rakhlin, A., Shamir, O., and Sridharan, K · 2012
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Private learning and sanitization: Pure vs. approximate differential privacy
Beimel, A., Nissim, K., and Stemmer, U · 2013
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Local privacy and statistical minimax rates
Duchi, J. C., Jordan, M. I., and Wainwright, M. J · 2013
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Privacy and statistical risk: Formalisms and minimax bounds
Barber, R. F. and Duchi, J. C · 2014
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Private empirical risk minimization: Efficient algorithms and tight error bounds
Bassily, R., Smith, A., and Thakurta, A · 2014
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Fingerprinting codes and the price of approximate differential privacy
Bun, M., Ullman, J., and Vadhan, S · 2014
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The algorithmic foundations of differential privacy
Dwork, C., Roth, A., et al · 2014
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Rappor: Randomized aggregatable privacy-preserving ordinal response
Úlfar Erlingsson, Pihur, V., and Korolova, A · 2014
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Convex optimization: Algorithms and complexity
Bubeck, S. et al · 2015
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Robust traceability from trace amounts
Dwork, C., Smith, A., Steinke, T., Ullman, J., and Vadhan, S · 2015
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Model inversion attacks that exploit confidence information and basic countermeasures
Fredrikson, M., Jha, S., and Ristenpart, T · 2015
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Conservative or liberal? personalized differential privacy
Jorgensen, Z., Yu, T., and Cormode, G · 2015
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Deep learning with differential privacy
Abadi, M., Chu, A., Goodfellow, I., McMahan, H. B., Mironov, I., Talwar, K., and Zhang, L · 2016
Cited alongside, same era.
Differential privacy overview, 2016
Apple · 2016
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Concentrated differential privacy: Simplifications, extensions, and lower bounds
Bun, M. and Steinke, T · 2016
Cited alongside, same era.
Linear convergence of gradient and proximal-gradient methods under the polyak-łojasiewicz condition
Karimi, H., Nutini, J., and Schmidt, M · 2016
Cited alongside, same era.
Blender: Enabling local search with a hybrid differential privacy model
Avent, B., Korolova, A., Zeber, D., Hovden, T., and Livshits, B · 2017
Cited alongside, same era.
Make up your mind: The price of online queries in differential privacy
Bun, M., Steinke, T., and Ullman, J · 2017
Differentially private assouad, fano, and le cam
Acharya, J., Sun, Z., and Zhang, H · 2021
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Extracting training data from large language models
Carlini, N., Tramer, F., Wallace, E., Jagielski, M., Herbert-Voss, A., Lee, K., Roberts, A., Brown, T. B., Song, D., Erlingsson, U., et al · 2021
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Lecture notes for statistics 311/electrical engineering 377
Duchi, J · 2021
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Combining public and private data
Ferrando, C., Gillenwater, J., and Kulesza, A · 2021
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(nearly) dimension independent private erm with adagrad rates via publicly estimated subspaces
Kairouz, P., Diaz, M. R., Rush, K., and Thakurta, A · 2021
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Leveraging public data for practical private query release
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Cited alongside, same era.
Collecting telemetry data privately
Ding, B., Kulkarni, J., and Yekhanin, S · 2017
Cited alongside, same era.
Semi-supervised knowledge transfer for deep learning from private training data
Papernot, N., Abadi, M., Erlingsson, Ú., Goodfellow, I., and Talwar, K · 2017
Cited alongside, same era.
Membership inference attacks against machine learning models
Shokri, R., Stronati, M., Song, C., and Shmatikov, V · 2017
Cited alongside, same era.
Learning new words, March 14 2017
Thakurta, A. G., Vyrros, A. H., Vaishampayan, U. S., Kapoor, G., Freudiger, J., Sridhar, V. R., and Davidson, D · 2017
Cited alongside, same era.
Zagoruyko, S. and Komodakis, N · 2017
Cited alongside, same era.
Model-agnostic private learning
Bassily, R., Thakkar, O., and Guha Thakurta, A · 2018
Cited alongside, same era.
Liu, T., Vietri, G., Steinke, T., Ullman, J., and Wu, S · 2021
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Output perturbation for differentially private convex optimization with improved population loss bounds, runtimes and applications to private adversarial training
Lowy, A. and Razaviyayn, M · 2021
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Public data-assisted mirror descent for private model training
Amid, E., Ganesh, A., Mathews, R., Ramaswamy, S., Song, S., Steinke, T., Suriyakumar, V. M., Thakkar, O., and Thakurta, A · 2022
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Optimal algorithms for mean estimation under local differential privacy
Asi, H., Feldman, V., and Talwar, K · 2022
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Private estimation with public data
Bie, A., Kamath, G., and Singhal, V · 2022
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Covariance’s loss is privacy’s gain: Computationally efficient, private and accurate synthetic data
Boedihardjo, M., Strohmer, T., and Vershynin, R · 2022
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Unlocking high-accuracy differentially private image classification through scale
De, S., Berrada, L., Hayes, J., Smith, S. L., and Balle, B · 2022
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Optimal and differentially private data acquisition: Central and local mechanisms
Fallah, A., Makhdoumi, A., Malekian, A., and Ozdaglar, A · 2022
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Langevin diffusion: An almost universal algorithm for private euclidean (convex) optimization
Ganesh, A., Thakurta, A., and Upadhyay, J · 2022
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Mixed differential privacy in computer vision
Golatkar, A., Achille, A., Wang, Y.-X., Roth, A., Kearns, M., and Soatto, S · 2022
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Large scale transfer learning for differentially private image classification
Mehta, H., Thakurta, A., Kurakin, A., and Cutkosky, A · 2022
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Personalized pate: Differential privacy for machine learning with individual privacy guarantees
Mühl, C. and Boenisch, F · 2022
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Hyperparameter tuning with renyi differential privacy
Papernot, N. and Steinke, T · 2022
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Private distribution learning with public data: The view from sample compression
Ben-David, S., Bie, A., Canonne, C. L., Kamath, G., and Singhal, V · 2023
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Why is public pretraining necessary for private model training?, 2023
Ganesh, A., Haghifam, M., Nasr, M., Oh, S., Steinke, T., Thakkar, O., Thakurta, A., and Wang, L · 2023
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Choosing public datasets for private machine learning via gradient subspace distance, 2023
Gu, X., Kamath, G., and Wu, Z. S · 2023
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Algorithmically effective differentially private synthetic data
He, Y., Vershynin, R., and Zhu, Y · 2023
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Coupling public and private gradient provably helps optimization
Liu, R., Bu, Z., Wang, Y.-x., Zha, S., and Karypis, G · 2023
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Effectively using public data in privacy preserving machine learning
Nasr, M., Mahloujifar, S., Tang, X., Mittal, P., and Houmansadr, A · 2023
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Differentially private image classification by learning priors from random processes, 2023
Tang, X., Panda, A., Sehwag, V., and Mittal, P · 2023
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Pillar: How to make semi-private learning more effective
Pinto, F., Hu, Y., Yang, F., and Sanyal, A · 2024
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Public-data assisted private stochastic optimization: Power and limitations
Ullah, E., Menart, M., Bassily, R., Guzmán, C., and Arora, R · 2024
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