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Found 5 papers, 1 papers with code
A Zero Auxiliary Knowledge Membership Inference Attack on Aggregate Location Data
To measure the risk of an MIA performed by a realistic adversary, we develop the first Zero Auxiliary Knowledge (ZK) MIA on aggregate location data, which eliminates the need for an auxiliary dataset of real individual traces.
Lost in the Averages: A New Specific Setup to Evaluate Membership Inference Attacks Against Machine Learning Models
Taken together, our results show that current MIA evaluation is averaging the risk across datasets leading to inaccurate risk estimates, and the risk posed by attacks leveraging information about the target dataset to be potentially underestimated.
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.
Correlation inference attacks against machine learning models
Despite machine learning models being widely used today, the relationship between a model and its training dataset is not well understood.
