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Tight Auditing of Differentially Private Machine Learning.

, , , , , , , and . USENIX Security Symposium, page 1631-1648. USENIX Association, (2023)

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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)Are aligned neural networks adversarially aligned?, , , , , , , , , and 1 other author(s). CoRR, (2023)Scalable Extraction of Training Data from (Production) Language Models., , , , , , , , , and . 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)Phantom: General Trigger Attacks on Retrieval Augmented Language Generation., , , , , , , and . CoRR, (2024)Improving Deep Learning with Differential Privacy using Gradient Encoding and Denoising., , and . CoRR, (2020)Privacy-Preserving Recommender Systems with Synthetic Query Generation using Differentially Private Large Language Models., , , , , and . CoRR, (2023)Stealing Part of a Production Language Model., , , , , , , , , and 3 other author(s). CoRR, (2024)