A Decentralized Cloud-Edge Infrastructure for Social Commerce: Accelerating Privacy-Preserving LLM Inference via Trusted Execution Environments
Keywords:
Distributed Systems, Social Commerce, Trusted Execution Environments, Edge Computing, Large Language Models, Privacy-Preserving AI, Socio-Technical Infrastructure.Abstract
The convergence of social networking and electronic commerce, termed social commerce, has necessitated a paradigm shift in how personalized digital experiences are delivered. At the heart of this evolution is the deployment of Large Language Models (LLMs) to facilitate context-aware interactions, personalized recommendations, and automated customer service. However, the centralization of user data required for LLM inference poses significant privacy risks and creates substantial latency bottlenecks in high-throughput environments. This paper proposes a novel decentralized cloud-edge infrastructure designed specifically for the social commerce domain. By distributing LLM inference tasks across a continuum of edge nodes and cloud servers, the proposed architecture minimizes data movement and optimizes response times. Central to this infrastructure is the integration of hardware-assisted security through Trusted Execution Environments (TEEs), which ensure that sensitive user data remains encrypted and isolated even during the inference process. We provide an exhaustive system-level analysis of this framework, emphasizing the structural trade-offs between computational overhead and security guarantees. The discussion extends to broader socio-technical implications, including infrastructure governance, environmental sustainability of decentralized AI, and the policy challenges associated with hardware-verified privacy. Our findings suggest that a tiered, TEE-augmented approach not only enhances user privacy but also provides the necessary scalability for the next generation of global social commerce platforms.
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