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The results suggest that private dense retrieval is already practical for moderately sized, privacy-sensitive corpora when minute-scale latency is acceptable, and formulates a two-round cryptographic protocol that provides both guarantees.
This work introduces SCALPEL, a contrastive sparse autoencoder designed to learn more selective forget features and shows theoretically that contrastive training promotes target-selective features and that the selection score controls expected background knowledge perturbation.
The experiments show that the learned tree topology provides strong interpretability by consistently isolating minority subgroups, which provides a transparent mapping between the model architecture and the data's latent group structure, while yielding competitive robustness with state-of-the-art methods.
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