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Machine Learning with Membership Privacy using Adversarial Regularization., , and . ACM Conference on Computer and Communications Security, page 634-646. ACM, (2018)Privacy Side Channels in Machine Learning Systems., , , , , , , and . CoRR, (2023)Private Fine-tuning of Large Language Models with Zeroth-order Optimization., , , , and . CoRR, (2024)Scalable Extraction of Training Data from (Production) Language Models., , , , , , , , , and . CoRR, (2023)Are aligned neural networks adversarially aligned?, , , , , , , , , and 1 other author(s). CoRR, (2023)Blind Adversarial Network Perturbations., , and . CoRR, (2020)Extracting Training Data from Diffusion Models., , , , , , , , and . USENIX Security Symposium, page 5253-5270. USENIX Association, (2023)Mitigating Membership Inference Attacks by Self-Distillation Through a Novel Ensemble Architecture., , , , , , and . USENIX Security Symposium, page 1433-1450. USENIX Association, (2022)Improving Deep Learning with Differential Privacy using Gradient Encoding and Denoising., , and . CoRR, (2020)Effectively Using Public Data in Privacy Preserving Machine Learning., , , , and . ICML, volume 202 of Proceedings of Machine Learning Research, page 25718-25732. PMLR, (2023)