MLP-based membership inference attacks on DP location aggregates learn only a one-threshold rule, which underperforms under Laplace noise, and 200k training samples let them learn the better two-threshold rule.
A zero auxiliary knowledge membership inference attack on aggregate location data,
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Sub-optimal Learning in Meta-Classifier Attacks: A Study of Membership Inference on Differentially Private Location Aggregates
MLP-based membership inference attacks on DP location aggregates learn only a one-threshold rule, which underperforms under Laplace noise, and 200k training samples let them learn the better two-threshold rule.