Proof-at-the-Edge: zk-SNARKs for Privacy-Preserving Wearable IoT Health Monitoring

Abstract

Healthcare IoT systems must balance the need for continuous monitoring with strong guarantees of privacy and trust. We present ProofHealth, a zero-knowledge proof–based framework that shifts verification to the edge by generating zk-SNARKs on smartphones. In this design, wearable data is encrypted and accompanied by proofs that ensure only valid submissions are admitted to cloud storage, even on untrusted networks. We implement and evaluate ProofHealth under varying batch sizes, measuring latency, throughput, and proof size. Results show batching significantly improves per-sample efficiency while proof sizes remain constant at sub-kilobyte scale, enabling lightweight communication suitable for constrained devices. This demonstrates the practicality of proof-at-the-edge healthcare monitoring and establishes ProofHealth as a novel approach to secure and privacy-preserving health data collection.