| 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 | 459 | 2018 |
| 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 | 233 | 2021 |
| 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 | 179 | 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 | 157* | 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 | 84 | 2019 |
| Cryptanalytic extraction of neural network models N Carlini, M Jagielski, I Mironov Annual International Cryptology Conference, 189-218, 2020 | 55 | 2020 |
| 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 | 55 | 2020 |
| Threat Detection for Collaborative Adaptive Cruise Control in Connected Cars M Jagielski, N Jones, CW Lin, C Nita-Rotaru, S Shiraishi Proceedings of the 11th ACM Conference on Security & Privacy in Wireless and …, 2018 | 36 | 2018 |
| 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 | 30 | 2021 |
| Secure communication channel establishment: TLS 1.3 (over TCP fast open) vs. QUIC S Chen, S Jero, M Jagielski, A Boldyreva, C Nita-Rotaru European Symposium on Research in Computer Security, 404-426, 2019 | 15* | 2019 |
| Network and system level security in connected vehicle applications H Liang, M Jagielski, B Zheng, CW Lin, E Kang, S Shiraishi, C Nita-Rotaru, ... 2018 IEEE/ACM International Conference on Computer-Aided Design (ICCAD), 1-7, 2018 | 11 | 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 | 9 | 2022 |
| Counterfactual Memorization in Neural Language Models C Zhang, D Ippolito, K Lee, M Jagielski, F Tramèr, N Carlini arXiv preprint arXiv:2112.12938, 2021 | 3 | 2021 |
| How to Combine Membership-Inference Attacks on Multiple Updated Models M Jagielski, S Wu, A Oprea, J Ullman, R Geambasu arXiv preprint arXiv:2205.06369, 2022 | | 2022 |
| Subverting Fair Image Search with Generative Adversarial Perturbations A Ghosh, M Jagielski, C Wilson arXiv preprint arXiv:2205.02414, 2022 | | 2022 |
| Truth Serum: Poisoning Machine Learning Models to Reveal Their Secrets F Tramèr, R Shokri, AS Joaquin, H Le, M Jagielski, S Hong, N Carlini arXiv preprint arXiv:2204.00032, 2022 | | 2022 |
| Debugging Differential Privacy: A Case Study for Privacy Auditing F Tramer, A Terzis, T Steinke, S Song, M Jagielski, N Carlini arXiv preprint arXiv:2202.12219, 2022 | | 2022 |
| Membership Inference Attacks are More Powerful Against Updated Models M Jagielski, S Wu, A Oprea, J Ullman, R Geambasu 2021 Workshop on Theory and Practice of Differential Privacy, 2021 | | 2021 |
| Integrity and Privacy in Adversarial Machine Learning M Jagielski Northeastern University, 2021 | | 2021 |
| DOES DIFFERENTIAL PRIVACY DEFEAT DATA POI M Jagielski, A Oprea | | |
Alina OpreaNortheastern UniversityBestätigte E-Mail-Adresse bei neu.edu
