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Deep learning with differential privacy (DP) has garnered significant attention over the past years, leading to the development of numerous methods aimed at enhancing model accuracy and training efficiency.
Long short-term memory
Hochreiter, S. and Schmidhuber, J · 1997
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An introduction to statistical modeling of extreme values
Coles, S., Bawa, J., Trenner, L., and Dorazio, P · 2001
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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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Robust de-anonymization of large sparse datasets
Narayanan, A. and Shmatikov, V · 2008
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On the complexity of differentially private data release: Efficient algorithms and hardness results
Dwork, C., Naor, M., Reingold, O., Rothblum, G. N., and Vadhan, S · 2009
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De-anonymizing social networks
Narayanan, A. and Shmatikov, V · 2009
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Stochastic gradient descent with differentially private updates
Song, S., Chaudhuri, K., and Sarwate, A. D · 2013
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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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Learning phrase representations using RNN encoder-decoder for statistical machine translation
Cho, K., Van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., and Bengio, Y · 2014
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The algorithmic foundations of differential privacy
Dwork, C., Roth, A., et al · 2014
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Auto-encoding variational Bayes
Kingma, D. P. and Welling, M · 2014
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Efficient per-example gradient computations
Goodfellow, I · 2015
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The MovieLens datasets: History and context
Harper, F. M. and Konstan, J. A · 2015
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Image-based recommendations on styles and substitutes
McAuley, J., Targett, C., Shi, Q., and Van Den Hengel, A · 2015
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Privacy for free: Posterior sampling and stochastic gradient monte carlo
Wang, Y.-X., Fienberg, S., and Smola, A · 2015
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Deep learning with differential privacy
Abadi, M., Chu, A., Goodfellow, I. J., McMahan, H. B., Mironov, I., Talwar, K., and Zhang, L · 2016
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Towards bayesian deep learning: A framework and some existing methods
Wang, H. and Yeung, D.-Y · 2016
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Natural-parameter networks: A class of probabilistic neural networks
Wang, H., Shi, X., and Yeung, D.-Y · 2016
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Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I · 2017
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Privacy amplification by subsampling: Tight analyses via couplings and divergences
Balle, B., Barthe, G., and Gaboardi, M · 2018
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Lightweight probabilistic deep networks
Gast, J. and Roth, S · 2018
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Self-attentive sequential recommendation
Kang, W.-C. and McAuley, J · 2018
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The Secret Sharer: Evaluating and testing unintended memorization in neural networks
Carlini, N., Liu, C., Erlingsson, Ú., Kos, J., and Song, D · 2019
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Sampling-free epistemic uncertainty estimation using approximated variance propagation
Postels, J., Ferroni, F., Coskun, H., Navab, N., and Tombari, F · 2019
Cited alongside, same era.
Feed-forward propagation in probabilistic neural networks with categorical and max layers
Shekhovtsov, A. and Flach, B · 2019
Cited alongside, same era.
Subsampled Rényi differential privacy and analytical moments accountant
Wang, Y.-X., Balle, B., and Kasiviswanathan, S. P · 2019
Cited alongside, same era.
Does learning require memorization? A short tale about a long tail
Feldman, V · 2020
Cited alongside, same era.
Privacy-and utility-preserving textual analysis via calibrated multivariate perturbations
Feyisetan, O., Balle, B., Drake, T., and Diethe, T · 2020
Cited alongside, same era.
Improving Transformer optimization through better initialization
Large-scale differentially private BERT
Anil, R., Ghazi, B., Gupta, V., Kumar, R., and Manurangsi, P · 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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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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High dimensional differentially private stochastic optimization with heavy-tailed data
Hu, L., Ni, S., Xiao, H., and Wang, D · 2022
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Improved rates for differentially private stochastic convex optimization with heavy-tailed data
Kamath, G., Liu, X., and Zhang, H · 2022
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Huang, X. S., Perez, F., Ba, J., and Volkovs, M · 2020
Cited alongside, same era.
On sampled metrics for item recommendation
Krichene, W. and Rendle, S · 2020
Cited alongside, same era.
Training production language models without memorizing user data
Ramaswamy, S., Thakkar, O., Mathews, R., Andrew, G., McMahan, H. B., and Beaufays, F · 2020
Cited alongside, same era.
On differentially private stochastic convex optimization with heavy-tailed data
Wang, D., Xiao, H., Devadas, S., and Xu, J · 2020
Cited alongside, same era.
A survey on Bayesian deep learning
Wang, H. and Yeung, D.-Y · 2020
Cited alongside, same era.
Private adaptive gradient methods for convex optimization
Asi, H., Duchi, J., Fallah, A., Javidbakht, O., and Talwar, K · 2021
Cited alongside, same era.
Scaling up differentially private deep learning with fast per-example gradient clipping
Lee, J. and Kifer, D · 2021
Cited alongside, same era.
Kolluri, A., Baluta, T., Hooi, B., and Saxena, P · 2022
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The limits of word level differential privacy
Mattern, J., Weggenmann, B., and Kerschbaum, F · 2022
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The role of adaptive optimizers for honest private hyperparameter selection
Mohapatra, S., Sasy, S., He, X., Kamath, G., and Thakkar, O · 2022
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Hyperparameter tuning with renyi differential privacy
Papernot, N. and Steinke, T · 2022
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Considerations for differentially private learning with large-scale public pretraining
Tramèr, F., Kamath, G., and Carlini, N · 2022
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DPIS: An enhanced mechanism for differentially private SGD with importance sampling
Wei, J., Bao, E., Xiao, X., and Yang, Y · 2022
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Normalized/Clipped SGD with perturbation for differentially private non-convex optimization
Yang, X., Zhang, H., Chen, W., and Liu, T.-Y · 2022
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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 · 2022
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TEM: High utility metric differential privacy on text
Carvalho, R. S., Vasiloudis, T., Feyisetan, O., and Wang, K · 2023
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DP-Forward: Fine-tuning and inference on language models with differential privacy in forward pass
Du, M., Yue, X., Chow, S. S. M., Wang, T., Huang, C., and Sun, H · 2023
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Exploring the limits of differentially private deep learning with group-wise clipping
He, J., Li, X., Yu, D., Zhang, H., Kulkarni, J., Lee, Y. T., Backurs, A., Yu, N., and Bian, J · 2023
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Differentially private sharpness-aware training
Park, J., Kim, H., Choi, Y., and Lee, J · 2023
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Losing less: A loss for differentially private deep learning
Shamsabadi, A. S. and Papernot, N · 2023
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Locally differentially private document generation using zero shot prompting
Utpala, S., Hooker, S., and Chen, P.-Y · 2023
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A theory to instruct differentially-private learning via clipping bias reduction
Xiao, H., Xiang, Z., Wang, D., and Devadas, S · 2023
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Vip: A differentially private foundation model for computer vision
Yu, Y., Sanjabi, M., Ma, Y., Chaudhuri, K., and Guo, C · 2023
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Split-and-denoise: Protect large language model inference with local differential privacy
Mai, P., Yan, R., Huang, Z., Yang, Y., and Pang, Y · 2024
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