Secure Transformer Inference via Nonlinear Operator Reduction and Efficient Multi-Party Computation
Keywords:
secure transformer inference, multi-party computation, nonlinear operator reduction, privacy-preserving machine learning, system architecture, governance, sustainabilityAbstract
Transformer architectures have become the dominant computational substrate for language understanding, multimodal reasoning, and generative artificial intelligence, yet their deployment increasingly collides with legal, commercial, and ethical expectations around data confidentiality. Secure transformer inference through multi-party computation offers a principled cryptographic path toward computation without disclosure, but the practical cost of nonlinear operators remains a central bottleneck. This paper examines secure transformer inference as a socio-technical systems problem in which nonlinear operator reduction, efficient multi-party computation protocols, infrastructure design, governance, and sustainability are mutually constitutive. It argues that reducing nonlinear operators is not merely an accuracy-latency trade-off but a structural intervention that reshapes threat models, hardware requirements, auditing practices, and fairness guarantees. The analysis compares two-party and three-party computation, garbled circuits, secret sharing, homomorphic encryption, trusted execution environments, and hybrid architectures, showing that no single technique resolves the full inference pipeline. The paper further explores deployment trade-offs across cloud, edge, and federated environments, emphasizing that efficient secure inference depends on network topology, memory hierarchy, accelerator availability, and operational governance. It also considers robustness under approximation, fairness risks when privacy shields bias, and energy implications of cryptographic redundancy. The conclusion proposes a research and policy agenda centered on co-design, standardized benchmarks, formal verification, and accountable deployment. The overarching claim is that secure transformer inference will mature only when cryptographic efficiency, nonlinear operator design, and institutional accountability are treated as a single design space rather than separate optimization problems.
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