Zero-Knowledge Enabled Sensor Fusion: Verifiableand Privacy Assured Inference for IoT Edge Systems

Abstract

Abstract With the growing implementation of multi sensor Internet of Things (IoT) and edge AI systems, the concerns over data reliability,privacy, and verifiability have been intensified. Conventional fusion architectures rely on deep learning models that deliverhigh accuracy. However, they fail to ensure that inferences are provably correct or tamper resistant under missing, noisy, oradversarial data conditions. To address these challenges, this paper introduces the Zero-Knowledge Privacy Assured SensorFusion (ZK-PAS Fusion) framework. ZK-PAS Fusion integrates convex bounded imputation, attention driven multi sensorfusion, BiLSTM based temporal modeling, and recursive zero-knowledge proof aggregation within a unified architecture. Theframework assures correctness, privacy, and robustness through cryptographic commitments and circuit level verifiability.Experimental evaluation is performed on two large scale clinical datasets, namely, MIMIC-IV and eICU-CDR. The modeldemonstrates a superior performance and achieves 99.45 % accuracy, 99.57 % F1-score, and an AUROC of 0.989, surpassingstate of the art transformer and diffusion based baselines by up to 5.4 % in accuracy and 6.2 % in F1-score. The proof moduleattains a 40 ms average proving time, 0.4 KB proof size, and ≈ 46 % lower energy consumption compared to state of the art(SOTA) models. These results establish ZK-PAS Fusion as a verifiable, memory efficient, and privacy preserving AI frameworkfor real time, safety critical edge IoT deployments.