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Auditing Private Prediction
Feb. 15, 2024, 5:10 a.m. | Karan Chadha, Matthew Jagielski, Nicolas Papernot, Christopher Choquette-Choo, Milad Nasr
cs.CR updates on arXiv.org arxiv.org
Abstract: Differential privacy (DP) offers a theoretical upper bound on the potential privacy leakage of analgorithm, while empirical auditing establishes a practical lower bound. Auditing techniques exist forDP training algorithms. However machine learning can also be made private at inference. We propose thefirst framework for auditing private prediction where we instantiate adversaries with varying poisoningand query capabilities. This enables us to study the privacy leakage of four private prediction algorithms:PATE [Papernot et al., 2016], CaPC [Choquette-Choo …
adversaries algorithms arxiv auditing can cs.cr differential privacy framework machine machine learning prediction privacy private techniques training
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