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Auditing mechanisms for differential privacy use probabilistic means to empirically estimate the privacy level of an algorithm.
Membership inference attacks from first principles
Carlini, N., Chien, S., Nasr, M., Song, S., Terzis, A., and Tramer, F · 1914
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Obtaining confidence intervals for the risk ratio in cohort studies
Katz, D., Baptista, J., Azen, S., and Pike, M · 1978
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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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A statistical framework for differential privacy
Wasserman, L., and Zhou, S · 2010
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The composition theorem for differential privacy
Kairouz, P., Oh, S., and Viswanath, P · 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
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Wide residual networks
Zagoruyko, S., and Komodakis, N · 2016
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Rényi differential privacy
Mironov, I · 2017
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Membership inference attacks against machine learning models
Shokri, R., Stronati, M., Song, C., and Shmatikov, V · 2017
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Privacy risk in machine learning: Analyzing the connection to overfitting
Yeom, S., Giacomelli, I., Fredrikson, M., and Jha, S · 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
Cited alongside, same era.
Dong, J., Roth, A., and Su, W. J · 2019
Cited alongside, same era.
Evaluating differentially private machine learning in practice
Jayaraman, B., and Evans, D · 2019
Cited alongside, same era.
Comprehensive privacy analysis of deep learning: Passive and active white-box inference attacks against centralized and federated learning
Nasr, M., Shokri, R., and Houmansadr, A · 2019
Cited alongside, same era.
Auditing differentially private machine learning: How private is private sgd?
Jagielski, M., Ullman, J., and Oprea, A · 2020
Cited alongside, same era.
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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Measuring forgetting of memorized training examples
Jagielski, M., Thakkar, O., Tramer, F., Ippolito, D., Lee, K., Carlini, N., Wallace, E., Song, S., Thakurta, A., Papernot, N., et al · 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., Raff, E., Ferraro, F., and Testa, B · 2022
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Canife: Crafting canaries for empirical privacy measurement in federated learning
Maddock, S., Sablayrolles, A., and Stock, P · 2022
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Computing tight differential privacy guarantees using fft
Koskela, A., Jälkö, J., and Honkela, A · 2020
Cited alongside, same era.
Adversary instantiation: Lower bounds for differentially private machine learning
Nasr, M., Songi, S., Thakurta, A., Papernot, N., and Carlini, N · 2021
Cited alongside, same era.
Privacy of noisy stochastic gradient descent: More iterations without more privacy loss
Altschuler, J. M., and Talwar, K · 2022
Cited alongside, same era.
Reconstructing training data with informed adversaries
Balle, B., Cherubin, G., and Hayes, J · 2022
Cited alongside, same era.
Quantifying memorization across neural language models
Carlini, N., Ippolito, D., Jagielski, M., Lee, K., Tramer, F., and Zhang, C · 2022
Cited alongside, same era.
Stevens, T., Ngong, I. C., Darais, D., Hirsch, C., Slater, D., and Near, J. P · 2022
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Debugging differential privacy: A case study for privacy auditing
Tramèr, F., Terzis, A., Steinke, T., Song, S., Jagielski, M., and Carlini, N · 2022
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Debugging differential privacy: A case study for privacy auditing
Tramer, F., Terzis, A., Steinke, T., Song, S., Jagielski, M., and Carlini, N · 2022
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Differentially private learning needs hidden state (or much faster convergence)
Ye, J., and Shokri, R · 2022
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Bayesian estimation of differential privacy
Zanella-Béguelin, S., Wutschitz, L., Tople, S., Salem, A., Rühle, V., Paverd, A., Naseri, M., and Köpf, B · 2022
Later among the works it cites.
https://github.com/google/jax/pull/3646
fix prng key reuse in differential privacy example by mattjj · Pull Request #3646 · google/jax — github.com · 2023
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