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cs.LG2024
Differentially Private Active Learning: Balancing Effective Data Selection and Privacy
Kristian Schwethelm, Johannes Kaiser, Jonas Kuntzer +3
Active learning (AL) is a widely used technique for optimizing data labeling in machine learning by iteratively selecting, labeling, and training on the most informative data. Howe…
cs.LG2024
Visual Privacy Auditing with Diffusion Models
Kristian Schwethelm, Johannes Kaiser, Moritz Knolle +3
Data reconstruction attacks on machine learning models pose a substantial threat to privacy, potentially leaking sensitive information. Although defending against such attacks usin…
cs.LG2024
From Mean to Extreme: Formal Differential Privacy Bounds on the Success of Real-World Data Reconstruction Attacks
Anneliese Riess, Kristian Schwethelm, Johannes Kaiser +4
The gold standard for privacy in machine learning, Differential Privacy (DP), is often interpreted through its guarantees against membership inference. However, translating DP budg…