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Found 2 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.
Ultra-marginal Feature Importance: Learning from Data with Causal Guarantees
Scientists frequently prioritize learning from data rather than training the best possible model; however, research in machine learning often prioritizes the latter.
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