TL;DR
SpanKey introduces a lightweight, key-conditioned gating mechanism for neural network inference that controls access via subspace keys, with analytical insights and experimental validation on CIFAR-10 and MNIST.
Contribution
The paper proposes a novel subspace key injection method for neural network gating, along with analytical diagnostics and experimental evaluations demonstrating its effectiveness.
Findings
Effective gating on CIFAR-10 ResNet-18
Analytical results on key absorption and energy split
A threat discussion clarifies security limitations
Abstract
SpanKey is a lightweight way to gate inference without encrypting weights or chasing leaderboard accuracy on gated inference. The idea is to condition activations on secret keys. A basis matrix defines a low-dimensional key subspace ; during training we sample coefficients and form keys , then inject them into intermediate activations with additive or multiplicative maps and strength . Valid keys lie in ; invalid keys are sampled outside that subspace. We make three points. (i) Mechanism: subspace key injection and a multi-layer design space. (ii) Failure mode: key absorption, together with two analytical results (a Beta-energy split and margin-tail diagnostics), explains weak baseline separation in energy and margin terms -- these are not a security theorem. iii) Deny losses and experiments: Modes A--C and extensions, with…
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