Data Provenance Inference in Machine Learning. (arXiv:2211.13416v1 [cs.LG])

Unintended memorization of various information granularity has garnered
academic attention in recent years, e.g. membership inference and property
inference. How to inversely use this privacy leakage to facilitate real-world
applications is a growing direction; the current efforts include dataset
ownership inference and user auditing. Standing on the data lifecycle and ML
model production, we propose an inference process named Data Provenance
Inference, which is to infer the generation, collection or processing property
of the ML training data, to assist ML …

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