arXiv:2610.22659v1 Announce Type: new Abstract: Methods for training language models on sensitive data differ in how they balance capability gains against the risk of leaking private information. In this paper, we present a controlled comparison of data-level and training-level interventions for navigating the capability-privacy tradeoff on synthetic medical records with canaries to probe for leakage.
Exploring The Empirical Privacy Landscape of Synthetic Data
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