  Reconstruction attacks allow an adversary to regenerate data samples of the
training set using access to only a trained model. It has been recently shown
that simple heuristics can reconstruct data samples from language models,
making this threat scenario an important aspect of model release. Differential
privacy is a known solution to such attacks, but is often used with a
relatively large privacy budget (epsilon > 8) which does not translate to
meaningful guarantees. In this paper we show that, for a same mechanism, we can
derive privacy guarantees for reconstruction attacks that are better than the
traditional ones from the literature. In particular, we show that larger
privacy budgets do not protect against membership inference, but can still
protect extraction of rare secrets. We show experimentally that our guarantees
hold against various language models, including GPT-2 finetuned on
Wikitext-103.

Machine Learning;Artificial Intelligence;Cryptography and Security;Machine Learning