How much of my dataset did you use? Quantitative Data Usage Inference in Machine Learning
Keywords: Machine Learning, Privacy, Dataset Usage Inference, Dataset Ownership, Membership Inference Attack, Dataset Copyright
TL;DR: The first method to quantitatively and non-binarily answer the question ``How much has a dataset been used in the training of a given model?''
Abstract: How much of my data was used to train a machine learning model? This is a critical question for data owners assessing the risk of unauthorized usage of their data to train models. However, previous work mistakenly treats this as a binary problem—inferring whether all-or-none or any-or-none of the data was used—which is fragile when faced with real, non-binary data usage risks. To address this, we propose a fine-grained analysis called Dataset Usage Cardinality Inference (DUCI), which estimates the exact proportion of data used. Our algorithm, leveraging debiased membership guesses, matches the performance of the optimal MLE approach (with a maximum error <0.1) but with significantly lower (e.g., $300 \times$ less) computational cost.
Primary Area: alignment, fairness, safety, privacy, and societal considerations
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Submission Number: 5454
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