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ML models are ubiquitous in real world applications and are a constant focus of research.
When relaxations go bad: "differentially-private" machine learning
Jayaraman, B., & Evans, D. (2019b) · 1902
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Erlingsson, Ú., Mironov, I., Raghunathan, A., & Song, S. (2019b) · 1908
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R \ \backslash ’enyi differential privacy of the sampled gaussian mechanism
Mironov, I., Talwar, K., & Zhang, L. (2019) · 1908
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Differentially private objective perturbation: Beyond smoothness and convexity
Neel, S., Roth, A., Vietri, G., & Wu, Z. S. (2019) · 1909
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Exploring the limits of transfer learning with a unified text-to-text transformer
Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., & Liu, P. J. (2019) · 1910
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Adversarial learning of privacy-preserving and task-oriented representations
Xiao, T., Tsai, Y., Sohn, K., Chandraker, M., & Yang, M. (2019) · 1911
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Gradient perturbation is underrated for differentially private convex optimization
Yu, D., Zhang, H., Chen, W., Liu, T., & Yin, J. (2019) · 1911
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Reviewing and improving the gaussian mechanism for differential privacy
Zhao, J., Wang, T., Bai, T., Lam, K.-Y., Xu, Z., Shi, S., Ren, X., Yang, X., Liu, Y., & Yu, H. (2019) · 1911
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Evaluating differentially private machine learning in practice
Jayaraman, B., & Evans, D. (2019a) · 1912
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Randomized response: A survey technique for eliminating evasive answer bias
Warner, S. L. (1965) · 1965
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Classification and Regression Trees
Leo Breiman, C. J. S. R. O., Jerome Friedman (1984) · 1984
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Greedy function approximation: A gradient boosting machine
Friedman, J. (2000) · 2000
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A better bound gives a hundred rounds: Enhanced privacy guarantees via $f$-divergences
Asoodeh, S., Liao, J., Calmon, F. P., Kosut, O., & Sankar, L. (2020) · 2001
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Encode, shuffle, analyze privacy revisited: Formalizations and empirical evaluation
Erlingsson, U., Feldman, V., Mironov, I., Raghunathan, A., Song, S., Talwar, K., & Thakurta, A. (2020) · 2001
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The tree ensemble layer: Differentiability meets conditional computation
Hazimeh, H., Ponomareva, N., Mol, P., Tan, Z., & Mazumder, R. (2020) · 2002
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Practical privacy: the sulq framework
Blum, A., Dwork, C., McSherry, F., & Nissim, K. (2005) · 2005
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Language models are few-shot learners
Brown, T. B., Mann, B., & et al, N. R. (2020) · 2005
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Revisiting membership inference under realistic assumptions
Jayaraman, B., Wang, L., Evans, D., & Gu, Q. (2020) · 2005
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k-means++: The advantages of careful seeding
Arthur, D., & Vassilvitskii, S. (2006) · 2006
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On the effect of normalization layers on differentially private training of deep neural networks
Davody, A., Adelani, D. I., Kleinbauer, T., & Klakow, D. (2020) · 2006
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Extremely randomized trees
Geurts, P. (2003) · 2006
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Private stochastic non-convex optimization: Adaptive algorithms and tighter generalization bounds
Zhou, Y., Chen, X., Hong, M., Wu, Z. S., & Banerjee, A. (2020) · 2006
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Mechanism design via differential privacy
McSherry, F., & Talwar, K. (2007) · 2007
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Smooth sensitivity and sampling in private data analysis
Nissim, K., Raskhodnikova, S., & Smith, A. (2007) · 2007
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A stochastic approximation method
Robbins, H. E. (2007) · 2007
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The trade-offs of private prediction
van der Maaten, L., & Hannun, A. Y. (2020) · 2007
Earlier work this paper cites.
Privacy-preserving logistic regression
Chaudhuri, K., & Monteleoni, C. (2008) · 2008
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A note on differential privacy: Defining resistance to arbitrary side information
Kasiviswanathan, S. P., & Smith, A. D. (2008) · 2008
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On the intrinsic differential privacy of bagging
Liu, H., Jia, J., & Gong, N. Z. (2020) · 2008
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Np-hardness of euclidean sum-of-squares clustering
Aloise, D., Deshpande, A., Hansen, P., & Popat, P. (2009) · 2009
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Differential privacy and robust statistics
Dwork, C., & Lei, J. (2009) · 2009
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Scaling up differentially private deep learning with fast per-example gradient clipping
Lee, J., & Kifer, D. (2020) · 2009
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Privacy integrated queries: an extensible platform for privacy-preserving data analysis
McSherry, F. D. (2009) · 2009
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The graph neural network model
Scarselli, F., Gori, M., Tsoi, A. C., Hagenbuchner, M., & Monfardini, G. (2009) · 2009
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Stochastic convex optimization
Shalev-Shwartz, S., Shamir, O., Srebro, N., & Sridharan, K. (2009) · 2009
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Quantifying privacy leakage in graph embedding
Duddu, V., Boutet, A., & Shejwalkar, V. (2020) · 2010
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Differential privacy in new settings
Dwork, C. (2010) · 2010
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A simple and practical algorithm for differentially private data release
Hardt, M., Ligett, K., & McSherry, F. (2010) · 2010
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Adaptive bound optimization for online convex optimization
McMahan, H. B., & Streeter, M. (2010) · 2010
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Enabling fast differentially private sgd via just-in-time compilation and vectorization
Subramani, P., Vadivelu, N., & Kamath, G. (2020) · 2010
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A learning theory approach to non-interactive database privacy
Blum, A., Ligett, K., & Roth, A. (2011) · 2011
Earlier work this paper cites.
Sample complexity bounds for differentially private learning
Chaudhuri, K., & Hsu, D. (2011) · 2011
Earlier work this paper cites.
Differentially private empirical risk minimization
Chaudhuri, K., Monteleoni, C., & Sarwate, A. D. (2011) · 2011
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Adaptive subgradient methods for online learning and stochastic optimization
Duchi, J., Hazan, E., & Singer, Y. (2011) · 2011
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A firm foundation for private data analysis
Dwork, C. (2011) · 2011
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What can we learn privately?
Kasiviswanathan, S. P., Lee, H. K., Nissim, K., Raskhodnikova, S., & Smith, A. (2011) · 2011
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Differentially private learning needs better features (or much more data)
Tramèr, F., & Boneh, D. (2020) · 2011
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Private convex empirical risk minimization and high-dimensional regression
Kifer, D., Smith, A., & Thakurta, A. (2012) · 2012
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Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012) · 2012
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An adaptive mechanism for accurate query answering under differential privacy
Li, C., & Miklau, G. (2012) · 2012
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Practical bayesian optimization of machine learning algorithms. URL https://arxiv.org/abs/1206.2944
Snoek, J., Larochelle, H., & Adams, R. P. (2012) · 2012
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Wang, K., Dick, T., & Balcan, M. (2020) · 2012
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Functional mechanism: Regression analysis under differential privacy
Zhang, J., Zhang, Z., Xiao, X., Yang, Y., & Winslett, M. (2012) · 2012
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Private learning and sanitization: Pure vs. approximate differential privacy
Beimel, A., Nissim, K., & Stemmer, U. (2013) · 2013
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Broadening the scope of differential privacy using metrics
Chatzikokolakis, K., Andrés, M. E., Bordenabe, N. E., & Palamidessi, C. (2013) · 2013
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Stochastic gradient descent with differentially private updates
Song, S., Chaudhuri, K., & Sarwate, A. D. (2013) · 2013
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Private empirical risk minimization, revisited
Bassily, R., Smith, A. D., & Thakurta, A. (2014) · 2014
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The large margin mechanism for differentially private maximization
Chaudhuri, K., Hsu, D. J., & Song, S. (2014) · 2014
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The algorithmic foundations of differential privacy
Dwork, C., & Roth, A. (2014) · 2014
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Differentially private distributed logistic regression using private and public data
Ji, Z., Jiang, X., Wang, S., Xiong, L., & Ohno-Machado, L. (2014) · 2014
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Adam: A method for stochastic optimization. URL https://arxiv.org/abs/1412.6980
Kingma, D. P., & Ba, J. (2014) · 2014
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Private empirical risk minimization beyond the worst case: The effect of the constraint set geometry
Talwar, K., Thakurta, A., & Zhang, L. (2014) · 2014
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Privbayes: Private data release via bayesian networks
Zhang, J., Cormode, G., Procopiuc, C. M., Srivastava, D., & Xiao, X. (2014) · 2014
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Differentially private high-dimensional data publication via sampling-based inference
Chen, R., Xiao, Q., Zhang, Y., & Xu, J. (2015) · 2015
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Efficient per-example gradient computations. URL https://arxiv.org/abs/1510.01799
Goodfellow, I. (2015) · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, S., & Szegedy, C. (2015) · 2015
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The composition theorem for differential privacy
Kairouz, P., Oh, S., & Viswanath, P. (2015) · 2015
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Subsampled exponential mechanism: Differential privacy in large output spaces
Lantz, E., Boyd, K., & Page, D. (2015) · 2015
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Deep learning with differential privacy
Abadi, M., Chu, A., Goodfellow, I., McMahan, H. B., Mironov, I., Talwar, K., & Zhang, L. (2016) · 2016
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Patent: Learning new words. URL https://patents.justia.com/patent/9645998
Abhradeep Guha Thakurta, U. S. V. G. K. J. F. V. R. S. D. D., Andrew H. Vyrros (2016) · 2016
Cited alongside, same era.
Layer normalization. URL https://arxiv.org/abs/1607.06450
Ba, J. L., Kiros, J. R., & Hinton, G. E. (2016) · 2016
Cited alongside, same era.
Concentrated differential privacy: Simplifications, extensions, and lower bounds
Bun, M., & Steinke, T. (2016) · 2016
Cited alongside, same era.
Decision tree classification with differential privacy: A survey
Fletcher, S., & Islam, M. Z. (2016) · 2016
Cited alongside, same era.
Deep Learning
Goodfellow, I., Bengio, Y., & Courville, A. (2016) · 2016
Cited alongside, same era.
Differentially private query release through adaptive projection
Aydöre, S., Brown, W., Kearns, M., Kenthapadi, K., Melis, L., Roth, A., & Siva, A. A. (2021) · 2021
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Characterizing signal propagation to close the performance gap in unnormalized resnets
Brock, A., De, S., & Smith, S. L. (2021) · 2021
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On the convergence and calibration of deep learning with differential privacy
Bu, Z., Wang, H., & Long, Q. (2021) · 2021
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Data synthesis via differentially private markov random fields
Cai, K., Lei, X., Wei, J., & Xiao, X. (2021) · 2021
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DPNAS: neural architecture search for deep learning with differential privacy
Cheng, A., Wang, J., Zhang, X. S., Chen, Q., Wang, P., & Cheng, J. (2021) · 2021
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Minami, K., Arai, H., Sato, I., & Nakagawa, H. (2016) · 2016
Cited alongside, same era.
Papernot, N., Abadi, M., Erlingsson, U., Goodfellow, I., & Talwar, K. (2016) · 2016
Cited alongside, same era.
Differential privacy preservation for deep auto-encoders: An application of human behavior prediction
Phan, N., Wang, Y., Wu, X., & Dou, D. (2016) · 2016
Cited alongside, same era.
Lipschitz extensions for node-private graph statistics and the generalized exponential mechanism
Raskhodnikova, S., & Smith, A. (2016) · 2016
Cited alongside, same era.
Weight normalization: A simple reparameterization to accelerate training of deep neural networks
Salimans, T., & Kingma, D. P. (2016) · 2016
Cited alongside, same era.
Differentially private k-means clustering
Su, D., Cao, J., Li, N., Bertino, E., & Jin, H. (2016) · 2016
Cited alongside, same era.
Differentially private stochastic gradient descent for in-rdbms analytics
Wu, X., Kumar, A., Chaudhuri, K., Jha, S., & Naughton, J. F. (2016) · 2016
Cited alongside, same era.
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Differential privacy dynamics of langevin diffusion and noisy gradient descent
Chourasia, R., Ye, J., & Shokri, R. (2021) · 2021
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Node-level differentially private graph neural networks
Daigavane, A., Madan, G., Sinha, A., Thakurta, A. G., Aggarwal, G., & Jain, P. (2021) · 2021
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Antipodes of label differential privacy: PATE and ALIBI
Esmaeili, M. M., Mironov, I., Prasad, K., Shilov, I., & Tramer, F. (2021) · 2021
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Hiding among the clones: A simple and nearly optimal analysis of privacy amplification by shuffling
Feldman, V., McMillan, A., & Talwar, K. (2022) · 2021
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On deep learning with label differential privacy
Ghazi, B., Golowich, N., Kumar, R., Manurangsi, P., & Zhang, C. (2021) · 2021
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Learning and evaluating a differentially private pre-trained language model
Hoory, S., Feder, A., Tendler, A., Cohen, A., Erell, S., Laish, I., Nakhost, H., Stemmer, U., Benjamini, A., Hassidim, A., & Matias, Y. (2021) · 2021
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Deduplicating training data makes language models better
Lee, K., Ippolito, D., Nystrom, A., Zhang, C., Eck, D., Callison-Burch, C., & Carlini, N. (2021) · 2021
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Iterative methods for private synthetic data: Unifying framework and new methods
Liu, T., Vietri, G., & Wu, Z. S. (2021) · 2021
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Winning the NIST contest: A scalable and general approach to differentially private synthetic data
McKenna, R., Miklau, G., & Sheldon, D. (2021) · 2021
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Adversary instantiation: Lower bounds for differentially private machine learning
Nasr, M., Songi, S., Thakurta, A., Papemoti, N., & Carlin, N. (2021) · 2021
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Natural language understanding with privacy-preserving bert
Qu, C., Kong, W., Yang, L., Zhang, M., Bendersky, M., & Najork, M. (2021) · 2021
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Privacy preserving machine learning: Maintaining confidentiality and preserving trust. URL https://www.microsoft.com/en-us/research/blog/privacy-preserving-machine-learning-maintaining-confidentiality-and-preserving-trust/#r1
Ruehle, V., Sim, R., Sergey Yekhanin, a. N. C. M. C., Jones, D., Laine, K., Köpf, B., Teevan, J., Kleewein, J., & Rajmohan, S. (2021) · 2021
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Locally private graph neural networks
Sajadmanesh, S., & Gatica-Perez, D. (2021) · 2021
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Evading the curse of dimensionality in unconstrained private glms
Song, S., Steinke, T., Thakkar, O., & Thakurta, A. (2021) · 2021
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Benchmarking differentially private synthetic data generation algorithms
Tao, Y., McKenna, R., Hay, M., Machanavajjhala, A., & Miklau, G. (2021) · 2021
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A field guide to federated optimization
Wang, J., Charles, Z., Xu, Z., Joshi, G., McMahan, H. B., Al-Shedivat, M., Andrew, G., Avestimehr, S., Daly, K., Data, D., et al. (2021) · 2021
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Tuning large neural networks via zero-shot hyperparameter transfer
Yang, G., Hu, E. J., Babuschkin, I., Sidor, S., Liu, X., Farhi, D., Ryder, N., Pachocki, J., Chen, W., & Gao, J. (2021) · 2021
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Opacus: User-friendly differential privacy library in pytorch
Yousefpour, A., Shilov, I., Sablayrolles, A., Testuggine, D., Prasad, K., Malek, M., Nguyen, J., Gosh, S., Bharadwaj, A., Zhao, J., Cormode, G., & Mironov, I. (2021) · 2021
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Differentially private fine-tuning of language models
Yu, D., Naik, S., Backurs, A., Gopi, S., Inan, H. A., Kamath, G., Kulkarni, J., Lee, Y. T., Manoel, A., Wutschitz, L., et al. (2021) · 2021
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Adaptive privacy preserving deep learning algorithms for medical data
Zhang, X., Ding, J., Wu, M., Wong, S. T. C., Van Nguyen, H., & Pan, M. (2021) · 2021
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Differentially private pairwise learning revisited
Zhiyu Xue1, M. H., Shaoyang Yang, & Wang, D. (2021) · 2021
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Privacy of noisy stochastic gradient descent: More iterations without more privacy loss
Altschuler, J. M., & Talwar, K. (2022) · 2022
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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., & Thakurta, A. G. (2022) · 2022
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Faster rates of convergence to stationary points in differentially private optimization
Arora, R., Bassily, R., González, T., Guzmán, C., Menart, M., & Ullah, E. (2022) · 2022
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A critical review on the use (and misuse) of differential privacy in machine learning
Blanco-Justicia, A., Sá nchez, D., Domingo-Ferrer, J., & Muralidhar, K. (2022) · 2022
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Federated learning and privacy
Bonawitz, K., Kairouz, P., Mcmahan, B., & Ramage, D. (2022) · 2022
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What does it mean for a language model to preserve privacy?
Brown, H., Lee, K., Mireshghallah, F., Shokri, R., & Tramèr, F. (2022) · 2022
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Choquette-Choo, C. A., McMahan, H. B., Rush, K., & Thakurta, A. (2022) · 2022
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Beyond uniform lipschitz condition in differentially private optimization
Das, R., Kale, S., Xu, Z., Zhang, T., & Sanghavi, S. (2022) · 2022
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Unlocking high-accuracy differentially private image classification through scale
De, S., Berrada, L., Hayes, J., Smith, S. L., & Balle, B. (2022) · 2022
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Denisov, S., McMahan, B., Rush, K., Smith, A., & Thakurta, A. G. (2022) · 2022
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Learning with Privacy at Scale
Differential Privacy Team, Apple (2022) · 2022
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Label differential privacy via clustering
Esfandiari, H., Mirrokni, V., Syed, U., & Vassilvitskii, S. (2022) · 2022
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Protecting privacy in Facebook mobility data during the COVID-19 response
Facebook (2022) · 2022
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Cascaded diffusion models for high fidelity image generation
Ho, J., Saharia, C., Chan, W., Fleet, D. J., Norouzi, M., & Salimans, T. (2022) · 2022
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Klause, H., Ziller, A., Rueckert, D., Hammernik, K., & Kaissis, G. (2022) · 2022
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Toward training at imagenet scale with differential privacy
Kurakin, A., Song, S., Chien, S., Geambasu, R., Terzis, A., & Thakurta, A. (2022) · 2022
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Differentially private accelerated optimization algorithms
Kuru, N., Ilker Birbil, S., Gurbuzbalaban, M., & Yildirim, S. (2022) · 2022
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A general framework for auditing differentially private machine learning
Lu, F., Munoz, J., Fuchs, M., LeBlond, T., Zaresky-Williams, E. V., Raff, E., Ferraro, F., & Testa, B. (2022) · 2022
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AIM: an adaptive and iterative mechanism for differentially private synthetic data
McKenna, R., Mullins, B., Sheldon, D., & Miklau, G. (2022) · 2022
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Supplement code for the blog "federated learning with formal differential privacy guarantees"
McMahan, H. B., & Thakurta, A. (2022) · 2022
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Differentially private image classification from features
Mehta, H., Krichene, W., Thakurta, A., Kurakin, A., & Cutkosky, A. (2022) · 2022
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Hyperparameter tuning with renyi differential privacy
Papernot, N., & Steinke, T. (2022) · 2022
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Training text-to-text transformers with privacy guarantees
Ponomareva, N., Bastings, J., & Vassilvitskii, S. (2022) · 2022
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Scaling up models and data with t5x
Roberts, A., Chung, H. W., & Levskaya, A. e. a. (2022) · 2022
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Tan without a burn: Scaling laws of dp-sgd. URL https://arxiv.org/abs/2210.03403
Sander, T., Stock, P., & Sablayrolles, A. (2022) · 2022
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Differential privacy at snapchat. URL https://eng.snap.com/en-US/differential-privacy-at-snapchat
Snapchat (2022) · 2022
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Stock, P., Shilov, I., Mironov, I., & Sablayrolles, A. (2022) · 2022
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Federated learning with formal differential privacy guarantees. URL https://ai.googleblog.com/2022/02/federated-learning-with-formal.html
Thakurta, A., & McMahan, B. (2022) · 2022
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Tramèr, F., Kamath, G., & Carlini, N. (2022) · 2022
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Differential Privacy 101
United States Census Bureau (2022) · 2022
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On the unreasonable effectiveness of federated averaging with heterogeneous data
Wang, J., Das, R., Joshi, G., Kale, S., Xu, Z., & Zhang, T. (2022) · 2022
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Wu, R., Zhou, J. P., Weinberger, K. Q., & Guo, C. (2022) · 2022
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Learning to generate image embeddings with user-level differential privacy
Xu, Z., Collins, M., Wang, Y., Panait, L., Oh, S., Augenstein, S., Liu, T., Schroff, F., & McMahan, H. B. (2022) · 2022
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Neural network weights do not converge to stationary points: An invariant measure perspective
Zhang, J., Li, H., Sra, S., & Jadbabaie, A. (2022) · 2022
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One-shot empirical privacy estimation for federated learning
Andrew, G., Kairouz, P., Oh, S., Oprea, A., McMahan, H. B., & Suriyakumar, V. (2023) · 2023
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(amplified) banded matrix factorization: A unified approach to private training
Choquette-Choo, C. A., Ganesh, A., McKenna, R., McMahan, H. B., Rush, K., Thakurta, A., & Xu, Z. (2023) · 2023
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Challenges towards the next frontier in privacy
Cummings, R., Desfontaines, D., Evans, D., Geambasu, R., Jagielski, M., Huang, Y., Kairouz, P., Kamath, G., Oh, S., Ohrimenko, O., Papernot, N., Rogers, R., Shen, M., Song, S., Su, W., Terzis, A., Thakurta, A., Vassilvitskii, S., Wang, Y.-X., Xiong, L., Yekhanin, S., Yu, D., Zhang, H., & Zhang, W. (2023) · 2023
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CANIFE: Crafting canaries for empirical privacy measurement in federated learning
Maddock, S., Sablayrolles, A., & Stock, P. (2023) · 2023
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Tight auditing of differentially private machine learning
Nasr, M., Hayes, J., Steinke, T., Balle, B., Tramèr, F., Jagielski, M., Carlini, N., & Terzis, A. (2023) · 2023
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Unleashing the power of randomization in auditing differentially private ML
Pillutla, K., Andrew, G., Kairouz, P., McMahan, H. B., Oprea, A., & Oh, S. (2023) · 2023
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Privacy auditing with one (1) training run
Steinke, T., Nasr, M., & Jagielski, M. (2023) · 2023
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Federated learning of gboard language models with differential privacy
Xu, Z., Zhang, Y., Andrew, G., Choquette, C., Kairouz, P., McMahan, B., Rosenstock, J., & Zhang, Y. (2023) · 2023
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