The Internet of Things (IoT) is fundamentally reshaping various industries by enabling efficient data collection and sharing. However, the prevalence of centralized cloud architectures introduces significant latency and high computational burdens, which are often incompatible with the real-time demands of resource-constrained edge devices. While edge computing provides a viable alternative, critical issues of data privacy and integrity remain, as a single data breach can lead to severe consequences. To address this dual challenge of security and efficiency, we introduce a novel edge-based computing framework. Our approach leverages secret-sharing-based Secure Multi-Party Computation alongside a terminal data source auditing mechanism to achieve secure model inference. We developed highly optimized protocols for Neural Network inference layers, significantly reducing communication overhead by integrating both arithmetic and boolean secret sharing. An innovative secure comparison protocol further minimizes server interactions, thereby boosting the efficiency of nonlinear computations. In addition, our framework incorporates a signature scheme to verify data legitimacy. Experimental evaluations on public datasets confirm that our method surpasses existing solutions in overall performance and security, validating its practical feasibility in modern IoT environments.