| Advances and open problems in federated learning P Kairouz, HB McMahan Foundations and trends in machine learning 14 (1-2), 1-210, 2021 | 9913 | 2021 |
| Analyzing federated learning through an adversarial lens AN Bhagoji, S Chakraborty, P Mittal, S Calo International conference on machine learning, 634-643, 2019 | 1736 | 2019 |
| Robustbench: a standardized adversarial robustness benchmark F Croce, M Andriushchenko, V Sehwag, E Debenedetti, N Flammarion, ... arXiv preprint arXiv:2010.09670, 2020 | 1042 | 2020 |
| Fine-tuning aligned language models compromises safety, even when users do not intend to! X Qi, Y Zeng, T Xie, PY Chen, R Jia, P Mittal, P Henderson arXiv preprint arXiv:2310.03693, 2023 | 973 | 2023 |
| Sybilinfer: Detecting sybil nodes using social networks. G Danezis, P Mittal Ndss 9, 1-15, 2009 | 645 | 2009 |
| EASiER: Encryption-based access control in social networks with efficient revocation S Jahid, P Mittal, N Borisov Proceedings of the 6th ACM Symposium on Information, Computer and …, 2011 | 579 | 2011 |
| Systematic evaluation of privacy risks of machine learning models L Song, P Mittal 30th USENIX security symposium (USENIX security 21), 2615-2632, 2021 | 575 | 2021 |
| Rocking drones with intentional sound noise on gyroscopic sensors Y Son, H Shin, D Kim, Y Park, J Noh, K Choi, J Choi, Y Kim 24th USENIX Security Symposium (USENIX Security 15), 881-896, 2015 | 558 | 2015 |
| Rocking drones with intentional sound noise on gyroscopic sensors Y Son, H Shin, D Kim, Y Park, J Noh, K Choi, J Choi, Y Kim 24th USENIX Security Symposium (USENIX Security 15), 881-896, 2015 | 558 | 2015 |
| Ssd: A unified framework for self-supervised outlier detection V Sehwag, M Chiang, P Mittal arXiv preprint arXiv:2103.12051, 2021 | 496 | 2021 |
| {BlackIoT}:{IoT} botnet of high wattage devices can disrupt the power grid S Soltan, P Mittal, HV Poor 27th USENIX security symposium (USENIX security 18), 15-32, 2018 | 491 | 2018 |
| Falcon: Honest-majority maliciously secure framework for private deep learning S Wagh, S Tople, F Benhamouda, E Kushilevitz, P Mittal, T Rabin arXiv preprint arXiv:2004.02229, 2020 | 421 | 2020 |
| Visual adversarial examples jailbreak aligned large language models X Qi, K Huang, A Panda, P Henderson, M Wang, P Mittal Proceedings of the AAAI conference on artificial intelligence 38 (19), 21527 …, 2024 | 384 | 2024 |
| {BotGrep}: Finding {p2p} bots with structured graph analysis S Nagaraja, P Mittal, CY Hong, M Caesar, N Borisov 19th USENIX Security Symposium (USENIX Security 10), 2010 | 373 | 2010 |
| Privacy risks of securing machine learning models against adversarial examples L Song, R Shokri, P Mittal Proceedings of the 2019 ACM SIGSAC conference on computer and communications …, 2019 | 370 | 2019 |
| Darts: Deceiving autonomous cars with toxic signs C Sitawarin, AN Bhagoji, A Mosenia, M Chiang, P Mittal arXiv preprint arXiv:1802.06430, 2018 | 340 | 2018 |
| {RAPTOR}: Routing attacks on privacy in tor Y Sun, A Edmundson, L Vanbever, O Li, J Rexford, M Chiang, P Mittal 24th USENIX Security Symposium (USENIX Security 15), 271-286, 2015 | 335 | 2015 |
| Mariana Raykova, Dawn Song, Weikang Song, Sebastian U P Kairouz, HB McMahan, B Avent, A Bellet, M Bennis, AN Bhagoji, ... Stich, Ziteng Sun, Ananda Theertha Suresh, Florian Tramèr, Praneeth …, 2021 | 314 | 2021 |
| Dependence makes you vulnberable: Differential privacy under dependent tuples. C Liu, S Chakraborty, P Mittal NDSS 16, 21-24, 2016 | 297 | 2016 |
| Enhancing robustness of machine learning systems via data transformations AN Bhagoji, D Cullina, C Sitawarin, P Mittal 2018 52nd Annual conference on information sciences and systems (CISS), 1-5, 2018 | 286 | 2018 |
Vikash SehwagGoogle Deepmind; Princeton UniversityVerified email at princeton.edu
