International Journal For Multidisciplinary Research

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AI CHAT INTEGRATION: MODEL CONTEXT PROTOCOL WITH HOMOMORPHIC ENCRYPTION (FHE)

Author(s) Mr. Chandan Jain H P
Country India
Abstract The rapid growth of conversational artificial intelligence (AI) across sectors has amplified concerns over data confidentiality. While conventional chat systems secure data in transit and at rest, they decrypt content at the server for inference or tool calls, creating vulnerabilities to unauthorized access. Existing solutions such as Fully Homomorphic Encryption (FHE) and confidential-compute techniques mitigate parts of this issue but face constraints in scalability, latency, or governance.
This paper introduces Secure Bridge, a privacy-first framework that eliminates default plaintext exposure while supporting flexible tool integration. The architecture integrates three components: (1) client-side encryption with FHE for supported computations, (2) a Model Context Protocol (MCP) gateway that enforces typed schemas, least-privilege access, and auditability, and (3) a confidential-compute fallback for complex or latency-sensitive tasks. A prototype using WebAssembly-based OpenFHE, React, Node.js, MongoDB, and Redis was tested under loads of up to 150 users. Results show sub-500 ms latency, error rates below 3%, and resilience against attacks, demonstrating Secure Bridge’s viability for regulated
Keywords Fully Homomorphic Encryption (FHE), Model Context Protocol (MCP), Privacy-preserving AI, Confidential Computing, Secure Chat Systems, Encrypted Inference, Policy-driven Governance
Field Computer > Network / Security
Published In Volume 7, Issue 6, November-December 2025
Published On 2025-11-25
DOI https://doi.org/10.36948/ijfmr.2025.v07i06.61192
Short DOI https://doi.org/hbcnv4
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