| Extracting training data from large language models N Carlini, F Tramer, E Wallace, M Jagielski, A Herbert-Voss, K Lee, ... 30th USENIX Security Symposium (USENIX Security 21), 2633-2650, 2021 | 1942 | 2021 |
| PaLM 2 Technical Report R Anil, AM Dai, O Firat, M Johnson, D Lepikhin, A Passos, S Shakeri, ... arXiv preprint arXiv:2305.10403, 2023 | 1558 | 2023 |
| Manipulating machine learning: Poisoning attacks and countermeasures for regression learning M Jagielski, A Oprea, B Biggio, C Liu, C Nita-Rotaru, B Li 2018 IEEE Symposium on Security and Privacy (SP), 19-35, 2018 | 1057 | 2018 |
| Quantifying Memorization Across Neural Language Models N Carlini, D Ippolito, M Jagielski, K Lee, F Tramer, C Zhang arXiv preprint arXiv:2202.07646, 2022 | 696 | 2022 |
| Extracting training data from diffusion models N Carlini, J Hayes, M Nasr, M Jagielski, V Sehwag, F Tramèr, B Balle, ... arXiv preprint arXiv:2301.13188, 2023 | 603 | 2023 |
| Why Do Adversarial Attacks Transfer? Explaining Transferability of Evasion and Poisoning Attacks A Demontis, M Melis, M Pintor, M Jagielski, B Biggio, A Oprea, ... 28th {USENIX} Security Symposium ({USENIX} Security 19), 321-338, 2019 | 508 | 2019 |
| High accuracy and high fidelity extraction of neural networks M Jagielski, N Carlini, D Berthelot, A Kurakin, N Papernot 29th USENIX security symposium (USENIX Security 20), 1345-1362, 2020 | 482 | 2020 |
| 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 | 281 | 2023 |
| 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 | 269 | 2024 |
| Auditing differentially private machine learning: How private is private sgd? M Jagielski, J Ullman, A Oprea Advances in Neural Information Processing Systems 33, 22205-22216, 2020 | 253 | 2020 |
| Differentially private fair learning M Jagielski, M Kearns, J Mao, A Oprea, A Roth, S Sharifi-Malvajerdi, ... International Conference on Machine Learning, 3000-3008, 2019 | 196 | 2019 |
| Poisoning web-scale training datasets is practical N Carlini, M Jagielski, CA Choquette-Choo, D Paleka, W Pearce, ... 2024 IEEE Symposium on Security and Privacy (SP), 407-425, 2024 | 191 | 2024 |
| Cryptanalytic extraction of neural network models N Carlini, M Jagielski, I Mironov Advances in Cryptology–CRYPTO 2020: 40th Annual International Cryptology …, 2020 | 161 | 2020 |
| Counterfactual memorization in neural language models C Zhang, D Ippolito, K Lee, M Jagielski, F Tramèr, N Carlini Advances in Neural Information Processing Systems 36, 39321-39362, 2023 | 146 | 2023 |
| Preventing generation of verbatim memorization in language models gives a false sense of privacy D Ippolito, F Tramèr, M Nasr, C Zhang, M Jagielski, K Lee, ... Proceedings of the 16th International Natural Language Generation Conference …, 2023 | 139* | 2023 |
| Subpopulation data poisoning attacks M Jagielski, G Severi, N Pousette Harger, A Oprea Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications …, 2021 | 131 | 2021 |
| Truth serum: Poisoning machine learning models to reveal their secrets F Tramèr, R Shokri, A San Joaquin, H Le, M Jagielski, S Hong, N Carlini Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications …, 2022 | 124 | 2022 |
| The privacy onion effect: Memorization is relative N Carlini, M Jagielski, C Zhang, N Papernot, A Terzis, F Tramer Advances in Neural Information Processing Systems 35, 13263-13276, 2022 | 101 | 2022 |
| Measuring Forgetting of Memorized Training Examples M Jagielski, O Thakkar, F Tramèr, D Ippolito, K Lee, N Carlini, E Wallace, ... arXiv preprint arXiv:2207.00099, 2022 | 94 | 2022 |
| 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 | 82 | 2023 |
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