Abadi, M., et al.: Deep learning with differential privacy. In: ACM SIGSAC Conference on Computer and Communications Security (CCS), pp. 308–318. ACM (2016)
Carlini, N., Chien, S., Nasr, M., Song, S., Terzis, A., Tramer, F.: Membership inference attacks from first principles. In: IEEE Symposium on Security and Privacy (S&P), p. 1519. IEEE (2022)
Carlini, N., et al.: Extracting training data from diffusion models. arXiv preprint arXiv:2301.13188 (2023)
Carlini, N., Liu, C., Erlingsson, Ú., Kos, J., Song, D.: The secret sharer: evaluating and testing unintended memorization in neural networks. In: USENIX Security Symposium (USENIX Security), pp. 267–284. USENIX Association (2019)
Carlini, N., et al.: Extracting training data from large language models. In: USENIX Security Symposium (USENIX Security), pp. 2633–2650. USENIX Association (2021)
Chen, D., Yu, N., Zhang, Y., Fritz, M.: GAN-leaks: a taxonomy of membership inference attacks against generative models. In: ACM SIGSAC Conference on Computer and Communications Security (CCS), pp. 343–362. ACM (2020)
Choquette-Choo, C.A., Tramer, F., Carlini, N., Papernot, N.: Label-only membership inference attacks. In: International Conference on Machine Learning (ICML), pp. 1964–1974. PMLR (2021)
Dhariwal, P., Nichol, A.: Diffusion models beat GANs on image synthesis. In: Advances in Neural Information Processing Systems (NeurIPS), vol. 34, pp. 8780– 8794. Curran Associates, Inc. (2021)
Dwork, C.: Differential privacy: a survey of results. In: Agrawal, M., Du, D., Duan, Z., Li, A. (eds.) TAMC 2008. LNCS, vol. 4978, pp. 1–19. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-79228-4 1
Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems (NeurIPS), pp. 2672–2680. Curran Associates, Inc. (2014)
Grathwohl, W., Chen, R.T., Bettencourt, J., Sutskever, I., Duvenaud, D.: FFJORD: free-form continuous dynamics for scalable reversible generative models. In: International Conference on Learning Representations (ICLR) (2018)
Hayes, J., Melis, L., Danezis, G., De Cristofaro, E.: LOGAN: membership inference attacks against generative models. In: Proceedings on Privacy Enhancing Technologies, pp. 133–152. Sciendo (2019)
Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: GANs trained by a two time-scale update rule converge to a local Nash equilibrium. In: Advances in Neural Information Processing Systems (NeurIPS), pp. 6626–6637. Curran Associates, Inc. (2017)
Hilprecht, B., Härterich, M., Bernau, D.: Monte Carlo and reconstruction membership inference attacks against generative models. In: Proceedings on Privacy Enhancing Technologies, pp. 232–249. Sciendo (2019)
Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. In: Advances in Neural Information Processing Systems (NeurIPS), vol. 33, pp. 6840–6851. Curran Associates, Inc. (2020)
Hu, H., Pang, J.: Membership inference attacks against GANs by leveraging over-representation regions. In: ACM SIGSAC Conference on Computer and Communications Security (CCS), pp. 2387–2389. ACM (2021)
Karras, T., Aittala, M., Aila, T., Laine, S.: Elucidating the design space of diffusion-based generative models. In: Advances in Neural Information Processing Systems (NeurIPS). Curran Associates, Inc. (2022)
Karras, T., Laine, S., Aila, T.: A style-based generator architecture for generative adversarial networks. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4401–4410. IEEE (2019)
Kazerouni, A., et al.: Diffusion models for medical image analysis: a comprehensive survey. arXiv preprint arXiv:2211.07804 (2022)
Leino, K., Fredrikson, M.: Stolen memories: leveraging model memorization for calibrated white-box membership inference. In: Proceedings of USENIX Security Symposium (USENIX Security), pp. 1605–1622. USENIX Association (2020)
Li, Z., Zhang, Y.: Membership leakage in label-only exposures. In: ACM SIGSAC Conference on Computer and Communications Security (CCS), pp. 880–895. ACM (2021)
Lin, Z., Jain, A., Wang, C., Fanti, G., Sekar, V.: Using GANs for sharing networked time series data: challenges, initial promise, and open questions. In: Proceedings of the ACM Internet Measurement Conference (IMC), pp. 464–483. ACM (2020)
Liu, Y., Zhao, Z., Backes, M., Zhang, Y.: Membership inference attacks by exploiting loss trajectory. In: ACM SIGSAC Conference on Computer and Communications Security (CCS), pp. 2085–2098 (2022)
Murakonda, S.K., Shokri, R.: ML privacy meter: aiding regulatory compliance by quantifying the privacy risks of machine learning. arXiv preprint arXiv:2007.09339 (2020)
Park, N., Mohammadi, M., Gorde, K., Jajodia, S., Park, H., Kim, Y.: Data synthesis based on generative adversarial networks. Proc. VLDB Endow. 11(10), 1071– 1083 (2018)
Parliament, E., of the European Union, C.: Art. 35 GDPR: Data protection impact assessment (2016). https://gdpr-info.eu/art-35-gdpr/
Pinaya, W.H.L., et al.: Brain imaging generation with latent diffusion models. In: Mukhopadhyay, A., Oksuz, I., Engelhardt, S., Zhu, D., Yuan, Y. (eds.) DGM4MICCAI 2022. LNCS, vol. 13609, pp. 117–126. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-18576-2 12
Rombach, R., Blattmann, A., Lorenz, D., Esser, P., Ommer, B.: High-resolution image synthesis with latent diffusion models. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 10684–10695. IEEE (2022)
Salem, A., Zhang, Y., Humbert, M., Berrang, P., Fritz, M., Backes, M.: ML-leaks: model and data independent membership inference attacks and defenses on machine learning models. In: Network and Distributed Systems Security Symposium (NDSS). Internet Society (2019)
Shokri, R., Stronati, M., Song, C., Shmatikov, V.: Membership inference attacks against machine learning models. In: IEEE Symposium on Security and Privacy (S&P), pp. 3–18. IEEE (2017)
Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: International Conference on Machine Learning (ICML), pp. 2256–2265. PMLR (2015)
Song, S., Marn, D.: Introducing a new privacy testing library in tensorflow (2020). https://blog.tensorflow.org/2020/06/introducing-new-privacy-testing-library. html
Song, Y., Ermon, S.: Generative modeling by estimating gradients of the data distribution. In: Advances in Neural Information Processing Systems (NeurIPS), vol. 32. Curran Associates, Inc. (2019)
Song, Y., Sohl-Dickstein, J., Kingma, D.P., Kumar, A., Ermon, S., Poole, B.: Score-based generative modeling through stochastic differential equations. In: International Conference on Learning Representations (ICLR) (2021)
Ye, J., Maddi, A., Murakonda, S.K., Shokri, R.: Enhanced membership inference attacks against machine learning models. In: ACM SIGSAC Conference on Computer and Communications Security (CCS), pp. 3093–3106 (2022)
Yousefpour, A., et al.: Opacus: user-friendly differential privacy library in PyTorch. arXiv preprint arXiv:2109.12298 (2021)
Zhu, D., Chen, D., Grossklags, J., Fritz, M.: Data forensics in diffusion models: a systematic analysis of membership privacy. arXiv preprint arXiv:2302.07801 (2023)
