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Privacy-Preserving machine learning (PPML) can help us train and deploy models that utilize private information.
Federated learning for ranking browser history suggestions
Hartmann, F.; Suh, S.; Komarzewski, A.; Smith, T. D.; and Segall, I. 2019 · 1911
Earlier work this paper cites.
Bayesian Hyperparameter Optimization with BoTorch, GPyTorch and Ax
Chang, D. T. 2021 · 1912
Earlier work this paper cites.
Membership inference attacks from first principles
Carlini, N.; Chien, S.; Nasr, M.; Song, S.; Terzis, A.; and Tramer, F. 2022 · 1914
Earlier work this paper cites.
Scaling up the accuracy of naive-bayes classifiers: A decision-tree hybrid
Kohavi, R.; et al. 1996 · 1996
Earlier work this paper cites.
idlg: Improved deep leakage from gradients
Zhao, B.; Mopuri, K. R.; and Bilen, H. 2020 · 2001
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Differential privacy
Dwork, C. 2006 · 2006
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Splintering with distributions: A stochastic decoy scheme for private computation
Vepakomma, P.; Balla, J.; and Raskar, R. 2020 · 2007
Earlier work this paper cites.
Resolving individuals contributing trace amounts of DNA to highly complex mixtures using high-density SNP genotyping microarrays
Homer, N.; Szelinger, S.; Redman, M.; Duggan, D.; Tembe, W.; Muehling, J.; Pearson, J. V.; Stephan, D. A.; Nelson, S. F.; and Craig, D. W. 2008 · 2008
Earlier work this paper cites.
Sapag: A self-adaptive privacy attack from gradients
Wang, Y.; Deng, J.; Guo, D.; Wang, C.; Meng, X.; Liu, H.; Ding, C.; and Rajasekaran, S. 2020 · 2009
Earlier work this paper cites.
Using data mining for bank direct marketing: An application of the crisp-dm methodology
Moro, S.; Laureano, R.; and Cortez, P. 2011 · 2011
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 · 2014
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. 2016a · 2016
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. 2016b · 2016
Earlier work this paper cites.
DeepFM: a factorization-machine based neural network for CTR prediction
Guo, H.; Tang, R.; Ye, Y.; Li, Z.; and He, X. 2017 · 2017
Earlier work this paper cites.
Communication-efficient learning of deep networks from decentralized data
McMahan, B.; Moore, E.; Ramage, D.; Hampson, S.; and y Arcas, B. A. 2017 · 2017
Cited alongside, same era.
Membership inference attacks against machine learning models
Shokri, R.; Stronati, M.; Song, C.; and Shmatikov, V. 2017 · 2017
Cited alongside, same era.
Distributed learning of deep neural network over multiple agents
Gupta, O.; and Raskar, R. 2018 · 2018
Cited alongside, same era.
Salem, A.; Zhang, Y.; Humbert, M.; Berrang, P.; Fritz, M.; and Backes, M. 2018 · 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 · 2018
Cited alongside, same era.
Fedvision: An online visual object detection platform powered by federated learning
Liu, Y.; Huang, A.; Luo, Y.; Huang, H.; Liu, Y.; Chen, Y.; Feng, L.; Chen, T.; Yu, H.; and Yang, Q. 2020 · 2020
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The future of digital health with federated learning
Rieke, N.; Hancox, J.; Li, W.; Milletari, F.; Roth, H. R.; Albarqouni, S.; Bakas, S.; Galtier, M. N.; Landman, B. A.; Maier-Hein, K.; et al. 2020 · 2020
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Differentially private learning with adaptive clipping
Andrew, G.; Thakkar, O.; McMahan, B.; and Ramaswamy, S. 2021 · 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 · 2021
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Fjord: Fair and accurate federated learning under heterogeneous targets with ordered dropout
Horvath, S.; Laskaridis, S.; Almeida, M.; Leontiadis, I.; Venieris, S.; and Lane, N. 2021 · 2021
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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 · 2019
Cited alongside, same era.
White-box vs black-box: Bayes optimal strategies for membership inference
Sablayrolles, A.; Douze, M.; Schmid, C.; Ollivier, Y.; and Jégou, H. 2019 · 2019
Cited alongside, same era.
Deep leakage from gradients
Zhu, L.; Liu, Z.; and Han, S. 2019 · 2019
Cited alongside, same era.
A survey on federated learning: The journey from centralized to distributed on-site learning and beyond
AbdulRahman, S.; Tout, H.; Ould-Slimane, H.; Mourad, A.; Talhi, C.; and Guizani, M. 2020 · 2020
Cited alongside, same era.
Rethinking privacy preserving deep learning: How to evaluate and thwart privacy attacks
Fan, L.; Ng, K. W.; Ju, C.; Zhang, T.; Liu, C.; Chan, C. S.; and Yang, Q. 2020 · 2020
Cited alongside, same era.
Inverting gradients-how easy is it to break privacy in federated learning?
Geiping, J.; Bauermeister, H.; Dröge, H.; and Moeller, M. 2020 · 2020
Cited alongside, same era.
How Apple personalizes Siri without hoovering up your data
Hao, K. 2020 · 2020
Cited alongside, same era.
Advances and open problems in federated learning
Kairouz, P.; McMahan, H. B.; Avent, B.; Bellet, A.; Bennis, M.; Bhagoji, A. N.; Bonawitz, K.; Charles, Z.; Cormode, G.; Cummings, R.; et al. 2021 · 2021
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A Novel CTR Prediction Model Based On DeepFM For Taobao Data
Li, L.; Hong, J.; Min, S.; and Xue, Y. 2021 · 2021
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Unleashing the tiger: Inference attacks on split learning
Pasquini, D.; Ateniese, G.; and Bernaschi, M. 2021 · 2021
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Federated evaluation and tuning for on-device personalization: System design & applications
Paulik, M.; Seigel, M.; Mason, H.; Telaar, D.; Kluivers, J.; van Dalen, R.; Lau, C. W.; Carlson, L.; Granqvist, F.; Vandevelde, C.; et al. 2021 · 2021
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On the importance of difficulty calibration in membership inference attacks
Watson, L.; Guo, C.; Cormode, G.; and Sablayrolles, A. 2021 · 2021
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See through gradients: Image batch recovery via gradinversion
Yin, H.; Mallya, A.; Vahdat, A.; Alvarez, J. M.; Kautz, J.; and Molchanov, P. 2021 · 2021
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Fel: High capacity learning for recommendation and ranking via federated ensemble learning
Hejazinia, M.; Huba, D.; Leontiadis, I.; Maeng, K.; Malek, M.; Melis, L.; Mironov, I.; Nasr, M.; Wang, K.; and Wu, C.-J. 2022 · 2022
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Splitfed: When federated learning meets split learning
Thapa, C.; Arachchige, P. C. M.; Camtepe, S.; and Sun, L. 2022 · 2022
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