Paper 2026/1733 Original: eprint.iacr.org/2026/1733 ↗

TEE-Assisted Authenticated MPC for Resource Constrained Edge_Intelligence

Abstract

Supporting privacy-preserving analytics across large Internet of Things (IoT) populations remains difficult. Resource-constrained devices may be unable to execute expensive multi-party computation (MPC) preprocessing or remain in a sustained many-party online protocol. We present a remotely attested, trusted execution environment (TEE)-assisted authenticated MPC multiplication scheme for large-scale edge intelligence. The TEE is deliberately restricted to the offline phase: before task-time compute committees are selected according to current edge availability and resource conditions, an Intel Software Guard Extensions (SGX) enclave provisions fine-grained authenticated material to authorized edge devices and then leaves the computation path. When a task arrives, a smaller set of $m<n$ compute committees aggregates the delegated device shares, the input owners inject their private data and model parameters through preprocessed masks, and the committees perform authenticated online multiplication without re-entering the TEE. We establish the correctness and authentication invariants of the committee-level computation and analyze the remotely attested deployment under an explicit TEE trust assumption. A hardware-SGX prototype implements the integrated scheme. The prototype generates batches of 10 million triples; at $l=1$ million in the evaluated three-recipient configuration, it achieves a $12.17\times$ preprocessing speedup over the evaluated MP-SPDZ MASCOT baseline. A separate stress test packages one preprocessing row for 100,000 recipients, and integrated tests reject ciphertext and online message-authentication-code (MAC) tampering. These results show that our scheme substantially improves preprocessing performance over the evaluated software-only protocol and is well suited to resource-constrained edge-intelligence deployments, underscoring its strong practical utility.