CC-Shield: A Unified Confidential ComputingFramework for Securing AI Model Training andInference

Abstract

Artificial-intelligence workloads increasingly process proprietary and personally identifiable data, yetconventional security controls protect data only at rest and in transit, leaving computation itself exposed. This paperpresents CC-Shield, a five-layer confidential-computing architecture that combines hardware trusted executionenvironments (Intel SGX, AMD SEV-SNP), differentially private federated aggregation, remote attestation, encryptedmodel lifecycle management, and LSTM-based anomaly detection into a single, formally analysed defence-in-depthstack. We derive a closed-form leakage bound that jointly composes TEE side-channel capacity and differential-privacynoise, prove three attack-resistance theorems covering membership inference, model inversion, and active-adversaryintegrity, and connect security overhead to system throughput via a queuing-theoretic performance model. On ResNet50/ImageNet, BERT-base/SST-2, and a clinical MLP on MIMIC-III, CC-Shield with differential privacy ( )reduces membership-inference attack success to 51.8% (statistically indistinguishable from the 50% random-chancebaseline at a 95% confidence half-width of approximately 1.0 percentage point over 10,000 attack queries), versus 71.3%for an unprotected baseline, while introducing only 11.9%-13.9% inference latency overhead – more than three ordersof magnitude lower than a homomorphic-encryption baseline. A seven-dimension qualitative comparison against fiveprior frameworks shows CC-Shield is the only approach satisfying data-in-use protection, computation integrity,training- and inference-time protection, quantum resistance, sub-15% latency overhead, and a formal security proofsimultaneously.