Kairouz, Peter

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. (2019) · arXiv

Privacy-Preserving Technologies in DataStochastic Gradient Optimization Techniques

Cited by 7,857

Advances and Open Problems in Federated Learning

Kairouz, P., & McMahan, H. B. (2021) · Foundations and Trends in Machine Learning

Privacy-Preserving Technologies in DataStochastic Gradient Optimization Techniques

Cited by 7,772

The composition theorem for differential privacy

Kairouz, P., Oh, S., & Viswanath, P. (2017) · IEEE Transactions on Information Theory

Privacy-Preserving Technologies in DataCloud Data Security Solutions

Cited by 408

Can You Really Backdoor Federated Learning?

Sun, Z., Kairouz, P., Suresh, A. T., & McMahan, H. B. (2019) · arXiv

Adversarial Robustness in Machine LearningPrivacy-Preserving Technologies in Data

Cited by 348

Back to the Drawing Board: A Critical Evaluation of Poisoning Attacks on Production Federated Learning

Shejwalkar, V., Houmansadr, A., Kairouz, P., & Ramage, D. (2022) · 2022 IEEE Symposium on Security and Privacy (SP)

Privacy-Preserving Technologies in DataAdversarial Robustness in Machine Learning

Cited by 292

Privacy-first health research with federated learning

Sadilek, A., Liu, L., Nguyen, D., Kamruzzaman, M., Serghiou, S., Rader, B., Ingerman, A., Mellem, S., Kairouz, P., Nsoesie, E. O., et al. (2021) · npj Digital Medicine

Privacy-Preserving Technologies in DataArtificial Intelligence in Healthcare and Education

Cited by 226

Context-Aware Generative Adversarial Privacy

Huang, C., Kairouz, P., Chen, X., Sankar, L., & Rajagopal, R. (2017) · Entropy

Privacy-Preserving Technologies in DataCloud Data Security Solutions

Cited by 156

A General Approach to Adding Differential Privacy to Iterative Training Procedures

McMahan, H. B., Andrew, G., Erlingsson, U., Chien, S., Mironov, I., Papernot, N., & Kairouz, P. (2018) · arXiv

Privacy-Preserving Technologies in DataStochastic Gradient Optimization Techniques

Cited by 126

The distributed discrete gaussian mechanism for federated learning with secure aggregation

Kairouz, P., Liu, Z., & Steinke, T. (2021) · arXiv

Privacy-Preserving Technologies in DataCryptography and Data Security

Cited by 117

Generative Models for Effective ML on Private, Decentralized Datasets

Augenstein, S., McMahan, H. B., Ramage, D., Ramaswamy, S., Kairouz, P., Chen, M., Mathews, R., & Arcas, B. A. Y. (2019) · arXiv

Privacy-Preserving Technologies in DataGenerative Adversarial Networks and Image Synthesis

Cited by 103

Practical and private (deep) learning without sampling or shuffling

Kairouz, P., McMahan, B., Song, S., Thakkar, O., Thakurta, A., & Xu, Z. (2021) · arXiv

Privacy-Preserving Technologies in DataStochastic Gradient Optimization Techniques

Cited by 52

AirGapAgent: Protecting Privacy-Conscious Conversational Agents

Bagdasarian, E., Yi, R., Ghalebikesabi, S., Kairouz, P., Gruteser, M., Oh, S., Balle, B., & Ramage, D. (2024) · Proceedings of the 2024 on ACM SIGSAC Conference on Computer and Communications Security

Privacy-Preserving Technologies in DataAdversarial Robustness in Machine Learning

Cited by 28

Generating Fair Universal Representations using Adversarial Models

Kairouz, P., Liao, J., Huang, C., Vyas, M., Welfert, M., & Sankar, L. (2019) · arXiv

Ethics and Social Impacts of AIPrivacy-Preserving Technologies in Data

Cited by 19

Shuffled Model of Federated Learning: Privacy, Communication and Accuracy Trade-offs

Girgis, A. M., Data, D., Diggavi, S., Kairouz, P., & Suresh, A. T. (2020) · arXiv

Privacy-Preserving Technologies in DataStochastic Gradient Optimization Techniques

Cited by 16