We introduce a new probabilistic method and task for quantifying the privacy risk of a document with personal attributes using large language models.

Easy-to-interpret, unified, tunable bounds on major operational attack risks in privacy-preserving ML and statistical releases that are more accurate than prior methods, using f-DP