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Privacy protection of users' entire contribution of samples is important in distributed systems.
Adaptive m-estimation in nonparametric regression
Hall, P., M. Jones · 1990
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Robust wavelet denoising
Sardy, S., P. Tseng, A. Bruce · 2001
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Robust statistics , vol. 523
Huber, P. J · 2004
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Calibrating noise to sensitivity in private data analysis
Dwork, C., F. McSherry, K. Nissim, et al · 2006
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Smooth sensitivity and sampling in private data analysis
Nissim, K., S. Raskhodnikova, A. Smith · 2007
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Differentially private recommender systems: Building privacy into the netflix prize contenders
McSherry, F., I. Mironov · 2009
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Differential privacy and robust statistics
Dwork, C., J. Lei · 2009
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On the point for which the sum of the distances to n given points is minimum
Weiszfeld, E., F. Plastria · 2009
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Boosting and differential privacy
Dwork, C., G. N. Rothblum, S. Vadhan · 2010
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What can we learn privately?
Kasiviswanathan, S. P., H. K. Lee, K. Nissim, et al · 2011
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Differentially private empirical risk minimization
Chaudhuri, K., C. Monteleoni, A. D. Sarwate · 2011
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Privacy-preserving statistical estimation with optimal convergence rates
Smith, A · 2011
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Hadamard matrix analysis and synthesis: with applications to communications and signal/image processing , vol. 383
Yarlagadda, R. K., J. E. Hershey · 2012
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The algorithmic foundations of differential privacy
Dwork, C., A. Roth, et al · 2014
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Personalized privacy-preserving frequent itemset mining using randomized response
Sun, C., Y. Fu, J. Zhou, et al · 2014
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Private empirical risk minimization: Efficient algorithms and tight error bounds
Bassily, R., A. Smith, A. Thakurta · 2014
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Privacy-preserving deep learning
Shokri, R., V. Shmatikov · 2015
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Weiszfeld’s method: Old and new results
Beck, A., S. Sabach · 2015
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An introduction to matrix concentration inequalities
Tropp, J. A., et al · 2015
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Collecting and analyzing data from smart device users with local differential privacy
Nguyên, T. T., X. Xiao, Y. Yang, et al · 2016
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Deep learning with differential privacy
Abadi, M., A. Chu, I. Goodfellow, et al · 2016
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Learning differentially private recurrent language models
McMahan, H. B., D. Ramage, K. Talwar, et al · 2017
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Differentially private federated learning: A client level perspective
Geyer, R. C., T. Klein, M. Nabi · 2017
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Being robust (in high dimensions) can be practical
Diakonikolas, I., G. Kamath, D. M. Kane, et al · 2017
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A general approach to adding differential privacy to iterative training procedures
McMahan, H. B., G. Andrew, U. Erlingsson, et al · 2018
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Private stochastic convex optimization with optimal rates
Bassily, R., V. Feldman, K. Talwar, et al · 2019
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Fair resource allocation in federated learning
Li, T., M. Sanjabi, A. Beirami, et al · 2019
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A tail-index analysis of stochastic gradient noise in deep neural networks
Simsekli, U., L. Sagun, M. Gurbuzbalaban · 2019
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Average-case averages: Private algorithms for smooth sensitivity and mean estimation
Bun, M., T. Steinke · 2019
Cited alongside, same era.
Robust estimators in high-dimensions without the computational intractability
Diakonikolas, I., G. Kamath, D. Kane, et al · 2019
Cited alongside, same era.
Private stochastic convex optimization: optimal rates in linear time
Feldman, V., T. Koren, K. Talwar · 2020
Cited alongside, same era.
Federated recommendation system via differential privacy
Li, T., L. Song, C. Fragouli · 2020
Cited alongside, same era.
Poisoning-assisted property inference attack against federated learning
Wang, Z., Y. Huang, M. Song, et al · 2022
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Mean estimation with user-level privacy under data heterogeneity
Cummings, R., V. Feldman, A. McMillan, et al · 2022
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Differential privacy and robust statistics in high dimensions
Liu, X., W. Kong, S. Oh · 2022
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Federated linear contextual bandits with user-level differential privacy
Huang, R., H. Zhang, L. Melis, et al · 2023
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User-level differential privacy with few examples per user
Ghazi, B., P. Kamath, R. Kumar, et al · 2023
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Improved convergence in high probability of clipped gradient methods with heavy tailed noise
Nguyen, T. D., T. H. Nguyen, A. Ene, et al · 2023
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Federated learning with differential privacy: Algorithms and performance analysis
Wei, K., J. Li, M. Ding, et al · 2020
Cited alongside, same era.
Learning discrete distributions: user vs item-level privacy
Liu, Y., A. T. Suresh, F. X. X. Yu, et al · 2020
Cited alongside, same era.
Self-balancing federated learning with global imbalanced data in mobile systems
Duan, M., D. Liu, X. Chen, et al · 2020
Cited alongside, same era.
Why are adaptive methods good for attention models?
Zhang, J., S. P. Karimireddy, A. Veit, et al · 2020
Cited alongside, same era.
Private mean estimation of heavy-tailed distributions
Kamath, G., V. Singhal, J. Ullman · 2020
Cited alongside, same era.
Instance-optimality in differential privacy via approximate inverse sensitivity mechanisms
Asi, H., J. C. Duchi · 2020
Cited alongside, same era.
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Attention-enhancing backdoor attacks against bert-based models
Lyu, W., S. Zheng, L. Pang, et al · 2023
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Label robust and differentially private linear regression: Computational and statistical efficiency
Liu, X., P. Jain, W. Kong, et al · 2023
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On robustness and local differential privacy
Li, M., T. B. Berrett, Y. Yu · 2023
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User-level private stochastic convex optimization with optimal rates
Bassily, R., Z. Sun · 2023
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Stability is stable: Connections between replicability, privacy, and adaptive generalization
Bun, M., M. Gaboardi, M. Hopkins, et al · 2023
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Discrete distribution estimation under user-level local differential privacy
Acharya, J., Y. Liu, Z. Sun · 2023
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From robustness to privacy and back
Asi, H., J. Ullman, L. Zakynthinou · 2023
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Algorithmic high-dimensional robust statistics
Diakonikolas, I., D. M. Kane · 2023
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Differentially private inference via noisy optimization
Avella-Medina, M., C. Bradshaw, P.-L. Loh · 2023
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Differentially private federated learning: A systematic review
Fu, J., Y. Hong, X. Ling, et al · 2024
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Trojvlm: Backdoor attack against vision language models
Lyu, W., L. Pang, T. Ma, et al · 2024
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Towards the robustness of differentially private federated learning
Qi, T., H. Wang, Y. Huang · 2024
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User-level differentially private stochastic convex optimization: Efficient algorithms with optimal rates
Liu, D., H. Asi · 2024
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Learning with user-level local differential privacy
Zhao, P., L. Shen, R. Fan, et al · 2024
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Better locally private sparse estimation given multiple samples per user
Ma, Y., K. Jia, H. Yang · 2024
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Robust nonparametric regression under poisoning attack
Zhao, P., Z. Wan · 2024
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A huber loss minimization approach to byzantine robust federated learning
Zhao, P., F. Yu, Z. Wan · 2024
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Private mean estimation with person-level differential privacy
Agarwal, S., G. Kamath, M. Majid, et al · 2024
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IPUMS USA: Version 15.0 [dataset], 2024
Ruggles, S., S. Flood, M. Sobek, et al · 2024
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