Joint Sensing-Security Optimization in Sensing-Integrated MLWE Under Noisy and Adversarial Environments

Abstract

Module Learning with Errors (MLWE) cryptography normally treats decryption noise as a disturbance: it must be large enough to hide algebraic structure but small enough for reliable decoding. This paper studies a conditional design in which selected residual degrees of freedom also carry coarse physical sensing information. The construction is not presented as a dropin replacement for standardised ML-KEM. Instead, we specify the assumptions under which residual-layer sensing can be analysed, identify what must remain external to FIPS-approved ML-KEM, and give a Fujisaki-Okamoto (FO)/CCA compatibility roadmap. The paper contributes an explicit measurement-to-label pipeline $\boldsymbol{z}=Q(F(y))$, concrete ML-KEM parameter instantiations, residual-budget calculations including compression noise, formal bounds for security-decomposition terms, higher-order leakage bounds beyond balanced mean suppression, an adaptive tuning algorithm, and residual-level Monte Carlo validation. The central message is that structured residual information can be useful for cyber-physical trust only when distributionshape closeness, decoding reliability, sensing privacy, and contextspoofing resistance are all quantified.