| Gemini: a family of highly capable multimodal models G Team, R Anil, S Borgeaud, Y Wu, JB Alayrac, J Yu, R Soricut, ... arXiv preprint arXiv:2312.11805, 2023 | 2146 | 2023 |
| Comprehensive Privacy Analysis of Deep Learning: Stand-alone and Federated Learning under Passive and Active White-box Inference Attacks M Nasr, R Shokri, A Houmansadr 2019 IEEE Symposium on Security and Privacy, 2019 | 1956* | 2019 |
| Universal and transferable adversarial attacks on aligned language models A Zou, Z Wang, N Carlini, M Nasr, JZ Kolter, M Fredrikson arXiv preprint arXiv:2307.15043, 2023 | 884 | 2023 |
| Membership inference attacks from first principles N Carlini, S Chien, M Nasr, S Song, A Terzis, F Tramer 2022 IEEE Symposium on Security and Privacy (SP), 1897-1914, 2022 | 632 | 2022 |
| Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context M Reid, N Savinov, D Teplyashin, D Lepikhin, T Lillicrap, J Alayrac, ... arXiv preprint arXiv:2403.05530, 2024 | 585 | 2024 |
| Extracting training data from diffusion models N Carlini, J Hayes, M Nasr, M Jagielski, V Sehwag, F Tramer, B Balle, ... 32nd USENIX Security Symposium (USENIX Security 23), 5253-5270, 2023 | 541 | 2023 |
| Machine Learning with Membership Privacy using Adversarial Regularization M Nasr, R Shokri, A Houmansadr Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications …, 2018 | 538 | 2018 |
| Are aligned neural networks adversarially aligned? N Carlini, M Nasr, CA Choquette-Choo, M Jagielski, I Gao, PWW Koh, ... Advances in Neural Information Processing Systems 36, 2024 | 236 | 2024 |
| Adversary Instantiation: Lower Bounds for Differentially Private Machine Learning M Nasr, S Song, A Thakurta, N Papernot, N Carlini 2021 IEEE Symposium on Security and Privacy, 2021 | 235 | 2021 |
| Scalable Extraction of Training Data from (Production) Language Models M Nasr, N Carlini, J Hayase, M Jagielski, AF Cooper, D Ippolito, ... arXiv preprint arXiv:2311.17035, 2023 | 228 | 2023 |
| DeepCorr: Strong Flow Correlation Attacks on Tor Using Deep Learning M Nasr, A Bahramali, A Houmansadr Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications …, 2018 | 217 | 2018 |
| Preventing Verbatim Memorization in Language Models Gives a False Sense of Privacy D Ippolito, F Tramèr, M Nasr, C Zhang, M Jagielski, K Lee, ... arXiv preprint arXiv:2210.17546, 2022 | 131 | 2022 |
| Defeating {DNN-Based} Traffic Analysis Systems in {Real-Time} With Blind Adversarial Perturbations M Nasr, A Bahramali, A Houmansadr 30th USENIX Security Symposium (USENIX Security 21), 2705-2722, 2021 | 126 | 2021 |
| Compressive Traffic Analysis: A New Paradigm for Scalable Traffic Analysis M Nasr, A Houmansadr, A Mazumdar Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications …, 2017 | 88 | 2017 |
| Mitigating Membership Inference Attacks by {Self-Distillation} Through a Novel Ensemble Architecture X Tang, S Mahloujifar, L Song, V Shejwalkar, M Nasr, A Houmansadr, ... 31st USENIX Security Symposium (USENIX Security 22), 1433-1450, 2022 | 86 | 2022 |
| Daemo: A Self-Governed Crowdsourcing Marketplace SN Gaikwad, D Morina, R Nistala, M Agarwal, A Cossette, R Bhanu, ... Adjunct Proceedings of the 28th Annual ACM Symposium on User Interface …, 2015 | 69 | 2015 |
| Tight auditing of differentially private machine learning M Nasr, J Hayes, T Steinke, B Balle, F Tramèr, M Jagielski, N Carlini, ... 32nd USENIX Security Symposium (USENIX Security 23), 1631-1648, 2023 | 64 | 2023 |
| Robust adversarial attacks against DNN-based wireless communication systems A Bahramali, M Nasr, A Houmansadr, D Goeckel, D Towsley Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications …, 2021 | 62 | 2021 |
| Privacy auditing with one (1) training run T Steinke, M Nasr, M Jagielski Advances in Neural Information Processing Systems 36, 2024 | 60 | 2024 |
| Stealing Part of a Production Language Model N Carlini, D Paleka, KD Dvijotham, T Steinke, J Hayase, AF Cooper, ... arXiv preprint arXiv:2403.06634, 2024 | 52 | 2024 |
Amir HoumansadrUniversity of Massachusetts AmherstBestätigte E-Mail-Adresse bei cs.umass.edu
