Mittal, Prateek

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

Analyzing Federated Learning through an Adversarial Lens

Bhagoji, A. N., Chakraborty, S., Mittal, P., & Calo, S. (2018) · arXiv

Adversarial Robustness in Machine LearningPrivacy-Preserving Technologies in Data

Cited by 703

RobustBench: a standardized adversarial robustness benchmark

Croce, F., Andriushchenko, M., Sehwag, V., Debenedetti, E., Flammarion, N., Chiang, M., Mittal, P., & Hein, M. (2020) · arXiv

Adversarial Robustness in Machine LearningSecurity and Verification in Computing

Cited by 284

Visual Adversarial Examples Jailbreak Aligned Large Language Models

Qi, X., Huang, K., Panda, A., Henderson, P., Wang, M., & Mittal, P. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence

Adversarial Robustness in Machine LearningDigital Media Forensic Detection

Cited by 150

Towards Probabilistic Verification of Machine Unlearning

Sommer, D. M., Song, L., Wagh, S., & Mittal, P. (2020) · arXiv

Privacy-Preserving Technologies in DataAdversarial Robustness in Machine Learning

Cited by 38

Certifiably Robust RAG against Retrieval Corruption

Xiang, C., Wu, T., Zhong, Z., Wagner, D., Chen, D., & Mittal, P. (2024) · arXiv

Adversarial Robustness in Machine LearningTopic Modeling

Cited by 7

Teach LLMs to Phish: Stealing Private Information from Language Models

Panda, A., Choquette-Choo, C. A., Zhang, Z., Yang, Y., & Mittal, P. (2024) · arXiv

Adversarial Robustness in Machine LearningPrivacy-Preserving Technologies in Data

Cited by 5