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Found 4 papers, 1 papers with code
Verifiable and Provably Secure Machine Unlearning
In this framework, the server first computes a proof that the model was trained on a dataset $D$.
Combing for Credentials: Active Pattern Extraction from Smart Reply
We show experimentally that it is possible for an adversary to extract sensitive user information present in the training data, even in realistic settings where all interactions with the model must go through a front-end that limits the types of queries.
Membership Inference on Word Embedding and Beyond
Indeed, our attack is a cheaper membership inference attack on text-generative models, which does not require the knowledge of the target model or any expensive training of text-generative models as shadow models.
Property Inference From Poisoning
In this work, we study property inference in scenarios where the adversary can maliciously control part of the training data (poisoning data) with the goal of increasing the leakage.
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