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Found 7 papers, 3 papers with code
Synthetic is all you need: removing the auxiliary data assumption for membership inference attacks against synthetic data
While membership inference attacks (MIAs), based on shadow modeling, have become the standard to evaluate the privacy of synthetic data, they currently assume the attacker to have access to an auxiliary dataset sampled from a similar distribution as the training dataset.
Achilles' Heels: Vulnerable Record Identification in Synthetic Data Publishing
The choice of vulnerable records is as important as more accurate MIAs when evaluating the privacy of synthetic data releases, including from a legal perspective.
Re-aligning Shadow Models can Improve White-box Membership Inference Attacks
Directly extending the shadow modelling technique from the black-box to the white-box setting has been shown, in general, not to perform better than black-box only attacks.
QuerySnout: Automating the Discovery of Attribute Inference Attacks against Query-Based Systems
We show the attacks found by QS to consistently equate or outperform, sometimes by a large margin, the best attacks from the literature.
WikiCREM: A Large Unsupervised Corpus for Coreference Resolution
We use a language-model-based approach for pronoun resolution in combination with our WikiCREM dataset.
A Surprisingly Robust Trick for the Winograd Schema Challenge
The Winograd Schema Challenge (WSC) dataset WSC273 and its inference counterpart WNLI are popular benchmarks for natural language understanding and commonsense reasoning.
A Surprisingly Robust Trick for Winograd Schema Challenge
The Winograd Schema Challenge (WSC) dataset WSC273 and its inference counterpart WNLI are popular benchmarks for natural language understanding and commonsense reasoning.
