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.
G. Saxena· International Journal of Mat...· 0 citations
The partition function is statistical mechanics'answer to an exponentially large spectrum, distilling it into a single analytic object whose temperature dependence resolves the full structure of the underlying ensemble. We show that magic, the resource separating universal quantum computation from classically simulable stabilizer dynamics, admits precisely such a description. Mapping the Pauli spectrum of a quantum state onto the energy levels of a fictitious many-body system, the Pauli gas, we construct its canonical partition function, the stabilizer partition function, and from its associated free energy a magic monotone that we call the stabilizer work. Both these objects are analytic functions of an inverse-temperature-like parameter and are efficiently estimable via Bell sampling. The framework is analytically tractable. We derive exact ensemble-averaged partition functions for Haar-random, $\nu$-compressible, and pseudomagic states, together with concentration guarantees. We show that for every value of its parameter, the stabilizer work is a faithful, Subadditive magic monotone, while remaining efficiently accessible on quantum hardware and admitting an operational interpretation. Unlike measures that probe a single moment of the Pauli distribution, the stabilizer work is intrinsically moment-generating. As the temperature is tuned from high to low, it interpolates continuously between the stabilizer 2-R\'enyi entropy and the stabilizer nullity, revealing two previously disconnected monotones as limiting cases of a single object. We demonstrate the framework on low-rank stabilizer simulation, resource interconversion, molecular ground states, and quantum many-body systems. A thermodynamics of magic is therefore not merely an analogy but a working toolkit, opening a statistical-mechanical route to magic properties of quantum systems that no single measure can access.
William E. Salazar, G. Saxena, Jack S Baker et al.· 0 citations