Reference Model Membership Inference (LBRM)
Reference Model Membership Inference (LBRM)
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A two-stage membership inference attack framework for time series imputation models that utilizes a reference model to improve the detection of training data inclusion.
Why It Matters
It is a novel membership inference attack specifically tailored for time series imputation models, designed to work even when models are robust to traditional overfitting-based attacks.
Evidence
a novel membership inference attack based on a reference model that improves detection accuracy
Related Papers
Metadata & Links
- created_at
- 2026-03-28T05:28:40Z