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No Free Lunch in "Privacy for Free: How does Dataset Condensation Help Privacy"., , and . CoRR, (2022)MassBrowser: Unblocking the Censored Web for the Masses, by the Masses., , , and . NDSS, The Internet Society, (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)Stealing part of a production language model., , , , , , , , , and 3 other author(s). ICML, OpenReview.net, (2024)Synthetic Query Generation for Privacy-Preserving Deep Retrieval Systems using Differentially Private Language Models., , , , , and . NAACL-HLT, page 3920-3930. Association for Computational Linguistics, (2024)Blind Adversarial Network Perturbations., , and . CoRR, (2020)Machine Learning with Membership Privacy using Adversarial Regularization., , and . ACM Conference on Computer and Communications Security, page 634-646. ACM, (2018)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)Scalable Extraction of Training Data from (Production) Language Models., , , , , , , , , and . CoRR, (2023)