LWM-CDE creates a structured representation space for wireless datasets using a foundation model that correlates better with empirical transfer performance than prior metrics.
The algorithmic foundations of differential privacy
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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ZK-PoP uses zero-knowledge proofs to attest that a writing session matches human typing patterns, without exposing the underlying behavioral data.
At typical differential privacy levels, Cox models lose significance for about 90% of covariates and drop to random predictive performance, with usable results requiring much weaker privacy.
citing papers explorer
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LWM-CDE: A Representation Space for Wireless Data Reasoning and Transferability
LWM-CDE creates a structured representation space for wireless datasets using a foundation model that correlates better with empirical transfer performance than prior metrics.
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Privacy-Preserving Proof of Human Authorship via Zero-Knowledge Process Attestation
ZK-PoP uses zero-knowledge proofs to attest that a writing session matches human typing patterns, without exposing the underlying behavioral data.
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Benchmarking the Utility of Privacy-Preserving Cox Regression Under Data-Driven Clipping Bounds: A Multi-Dataset Simulation Study
At typical differential privacy levels, Cox models lose significance for about 90% of covariates and drop to random predictive performance, with usable results requiring much weaker privacy.