Fetching the paper…
Reading the bibliography…
Differential privacy (DP) is by far the most widely accepted framework for mitigating privacy risks in machine learning.
On measures of entropy and information
Rényi, A. et al · 1961
Earlier work this paper cites.
Information measures and capacity of order α \alpha for discrete memoryless channels
Arimoto, S · 1977
Earlier work this paper cites.
Calibrating noise to sensitivity in private data analysis
Dwork, C., McSherry, F., Nissim, K., and Smith, A · 2006
Earlier work this paper cites.
Estimation of the warfarin dose with clinical and pharmacogenetic data
Klein, T., Altman, R., Eriksson, N., Gage, B., Kimmel, S., Lee, M.-T., Limdi, N., Page, D., Roden, D., Wagner, M., Caldwell, M., and Johnson, J · 2009
Earlier work this paper cites.
On the difficulties of disclosure prevention in statistical databases or the case for differential privacy
Dwork, C. and Naor, M · 2010
Earlier work this paper cites.
On the relation between differential privacy and quantitative information flow
Alvim, M. S., Andrés, M. E., Chatzikokolakis, K., and Palamidessi, C · 2011
Earlier work this paper cites.
Differentially private empirical risk minimization
Chaudhuri, K., Monteleoni, C., and Sarwate, A. D · 2011
Earlier work this paper cites.
Information-theoretic foundations of differential privacy
Mir, D. J · 2013
Earlier work this paper cites.
Privacy in pharmacogenetics: An { \{ End-to-End } \} case study of personalized warfarin dosing
Fredrikson, M., Lantz, E., Jha, S., Lin, S., Page, D., and Ristenpart, T · 2014
Earlier work this paper cites.
From the information bottleneck to the privacy funnel
Makhdoumi, A., Salamatian, S., Fawaz, N., and Médard, M · 2014
Earlier work this paper cites.
Model inversion attacks that exploit confidence information and basic countermeasures
Fredrikson, M., Jha, S., and Ristenpart, T · 2015
Earlier work this paper cites.
Privacy-preserving deep learning
Shokri, R. and Shmatikov, V · 2015
Earlier work this paper cites.
α \alpha -mutual information
Verdú, S · 2015
Earlier work this paper cites.
Deep learning with differential privacy
Abadi, M., Chu, A., Goodfellow, I., McMahan, H. B., Mironov, I., Talwar, K., and Zhang, L · 2016
Earlier work this paper cites.
Differential privacy as a mutual information constraint
Cuff, P. and Yu, L · 2016
Earlier work this paper cites.
f f -divergence inequalities
Sason, I. and Verdú, S · 2016
Earlier work this paper cites.
On the relation between identifiability, differential privacy, and mutual-information privacy
Wang, W., Ying, L., and Zhang, J · 2016
Cited alongside, same era.
Communication-efficient learning of deep networks from decentralized data
McMahan, B., Moore, E., Ramage, D., Hampson, S., and y Arcas, B. A · 2017
Cited alongside, same era.
Rényi differential privacy
Mironov, I · 2017
Cited alongside, same era.
On w w -mixtures: Finite convex combinations of prescribed component distributions
Nielsen, F. and Nock, R · 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.
Machine learning models that remember too much
Song, C., Ristenpart, T., and Shmatikov, V · 2017
Deep leakage from gradients
Zhu, L., Liu, Z., and Han, S · 2019
Later among the works it cites.
Inverting gradients-how easy is it to break privacy in federated learning?
Geiping, J., Bauermeister, H., Dröge, H., and Moeller, M · 2020
Later among the works it cites.
Differentially private learning does not bound membership inference
Humphries, T., Rafuse, M., Tulloch, L., Oya, S., Goldberg, I., Hengartner, U., and Kerschbaum, F · 2020
Later among the works it cites.
Auditing differentially private machine learning: How private is private sgd?
Jagielski, M., Ullman, J., and Oprea, A · 2020
Later among the works it cites.
Privacy-utility tradeoff and privacy funnel
Salamatian, S., Calmon, F. P., Fawaz, N., Makhdoumi, A., and Médard, M · 2020
Later among the works it cites.
Extracting training data from large language models
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Protection against reconstruction and its applications in private federated learning
Bhowmick, A., Duchi, J., Freudiger, J., Kapoor, G., and Rogers, R · 2018
Cited alongside, same era.
Salem, A., Zhang, Y., Humbert, M., Berrang, P., Fritz, M., and Backes, M · 2018
Cited alongside, same era.
Privacy risk in machine learning: Analyzing the connection to overfitting
Yeom, S., Giacomelli, I., Fredrikson, M., and Jha, S · 2018
Cited alongside, same era.
Adversarially learned representations for information obfuscation and inference
Bertran, M., Martinez, N., Papadaki, A., Qiu, Q., Rodrigues, M., Reeves, G., and Sapiro, G · 2019
Cited alongside, same era.
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.
Erlingsson, Ú., Mironov, I., Raghunathan, A., and Song, S · 2019
Cited alongside, same era.
Carlini, N., Tramer, F., Wallace, E., Jagielski, M., Herbert-Voss, A., Lee, K., Roberts, A., Brown, T., Song, D., Erlingsson, U., et al · 2021
Later among the works it cites.
Measuring data leakage in machine-learning models with fisher information
Hannun, A., Guo, C., and van der Maaten, L · 2021
Later among the works it cites.
Adversary instantiation: Lower bounds for differentially private machine learning
Nasr, M., Songi, S., Thakurta, A., Papernot, N., and Carlin, N · 2021
Later among the works it cites.
Improving robustness to model inversion attacks via mutual information regularization
Wang, T., Zhang, Y., and Jia, R · 2021
Later among the works it cites.
On the importance of difficulty calibration in membership inference attacks
Watson, L., Guo, C., Cormode, G., and Sablayrolles, A · 2021
Later among the works it cites.
Enhanced membership inference attacks against machine learning models
Ye, J., Maddi, A., Murakonda, S. K., and Shokri, R · 2021
Later among the works it cites.
Reconstructing training data with informed adversaries
Balle, B., Cherubin, G., and Hayes, J · 2022
Closest in time.
Membership inference attacks from first principles
Carlini, N., Chien, S., Nasr, M., Song, S., Terzis, A., and Tramer, F · 2022
Closest in time.
Bounding training data reconstruction in private (deep) learning
Guo, C., Karrer, B., Chaudhuri, K., and van der Maaten, L · 2022
Closest in time.
Optimal membership inference bounds for adaptive composition of sampled gaussian mechanisms
Mahloujifar, S., Sablayrolles, A., Cormode, G., and Jha, S · 2022
Closest in time.
Thudi, A., Shumailov, I., Boenisch, F., and Papernot, N · 2022
Closest in time.