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Membership Inference Attack (MIA) identifies whether a record exists in a machine learning model's training set by querying the model.
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J. Hayes, L. Melis, G. Danezis, and E. De Cristofaro, “LOGAN: Membership Inference Attacks Against Generative Models,” Proceedings on Privacy Enhancing Technologies
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B. Hilprecht, M. Härterich, and D. Bernau, “Monte Carlo and Reconstruction Membership Inference Attacks against Generative Models.,” Proc. Priv. Enhancing Technol
2019
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2020
Cited alongside, same era.
D. Chen, N. Yu, Y. Zhang, and M. Fritz, “GAN-Leaks: A Taxonomy of Membership Inference Attacks against Generative Models,” in Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security
2020
Cited alongside, same era.
J. Ho, A. Jain, and P. Abbeel, “Denoising diffusion probabilistic models,” in Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12, 2020, virtual
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2022
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C. Chadebec, L. J. Vincent, and S. Allassonniere, “Pythae: Unifying generative autoencoders in python - a benchmarking use case,” in Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track
2022
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L. Watson, C. Guo, G. Cormode, and A. Sablayrolles, “On the importance of difficulty calibration in membership inference attacks,” in Proc. of ICLR
2022
Later among the works it cites.
W. Fu, H. Wang, L. Zhang, C. Gao, Y. Li, and T. Jiang, “A probabilistic fluctuation based membership inference attack for diffusion models,” arXiv e-prints
2023
Closest in time.
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2021
Cited alongside, same era.
Y. Tashiro, J. Song, Y. Song, and S. Ermon, “CSDI: conditional score-based diffusion models for probabilistic time series imputation,” in Proc. of NeurIPS
2021
Cited alongside, same era.
2021
Cited alongside, same era.
H. Hu and J. Pang, “Membership Inference Attacks against GANs by Leveraging Over-representation Regions,” in Proc. of CCS
2021
Cited alongside, same era.
G. J. J. van den Burg and C. Williams, “On memorization in probabilistic deep generative models,” in Proc. of NeurIPS
2021
Cited alongside, same era.
J. Song, C. Meng, and S. Ermon, “Denoising diffusion implicit models,” in Proc. of ICLR
2021
Cited alongside, same era.
R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer, “High-Resolution Image Synthesis With Latent Diffusion Models,” in Proc. of CVPR
2022
Cited alongside, same era.
H. Hu, Z. Salcic, L. Sun, G. Dobbie, P. S. Yu, and X. Zhang, “Membership Inference Attacks on Machine Learning: A Survey,” ACM Computing Surveys
2022
Cited alongside, same era.
C. Stokel-Walker and R. Van Noorden, “What ChatGPT and generative AI mean for science,” Nature
2023
Closest in time.
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Y. K. Dwivedi, N. Kshetri, and L. e. a. Hughes, “Opinion Paper: “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy,” International Journal of Information Management
2023
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2023
Closest in time.
C. Zhang, D. Ippolito, K. Lee, M. Jagielski, F. Tramèr, and N. Carlini, “Counterfactual Memorization in Neural Language Models,” in Advances in Neural Information Processing Systems
2023
Closest in time.
D. Ippolito, F. Tramer, M. Nasr, C. Zhang, M. Jagielski, K. Lee, C. Choquette Choo, and N. Carlini, “Preventing Generation of Verbatim Memorization in Language Models Gives a False Sense of Privacy,” in Proceedings of the 16th International Natural Language Generation Conference
2023
Closest in time.
B. van Breugel, H. Sun, Z. Qian, and M. van der Schaar, “Membership Inference Attacks against Synthetic Data through Overfitting Detection,” in Proceedings of The 26th International Conference on Artificial Intelligence and Statistics
2023
Closest in time.
J. Duan, F. Kong, S. Wang, X. Shi, and K. Xu, “Are Diffusion Models Vulnerable to Membership Inference Attacks?,” in Proceedings of the 38th International Conference on Machine Learning, ICML 2023
2023
Closest in time.
J. Mattern, F. Mireshghallah, Z. Jin, B. Schölkopf, M. Sachan, and T. Berg-Kirkpatrick, “Membership Inference Attacks against Language Models via Neighbourhood Comparison,” 2023
2023
Closest in time.
N. Carlini, J. Hayes, M. Nasr, M. Jagielski, V. Sehwag, F. Tramèr, B. Balle, D. Ippolito, and E. Wallace, “Extracting Training Data from Diffusion Models,” in 32nd USENIX Security Symposium (USENIX Security 23)
2023
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2023
Closest in time.
C. Chadebec, E. Thibeau-Sutre, N. Burgos, and S. Allassonnière, “Data Augmentation in High Dimensional Low Sample Size Setting Using a Geometry-Based Variational Autoencoder,” IEEE Transactions on Pattern Analysis and Machine Intelligence
2023
Closest in time.