Method and system for browser-based processing of encrypted documents with paragraph-aware segmentation and cryptographic audit trail generation

Abstract

A method and system for processing encrypted documents using artificial intelligence within web browser environments while maintaining end-to-end encryption boundaries. The system decrypts documents in isolated browser memory, extracts text using multi-pass paragraph-aware segmentation comprising visual, linguistic, semantic, and domain-specific analysis passes with aggregator and voting logic for boundary determination, processes content through domain-specific AI modules with quantized neural network weights, and generates cryptographic audit trails using chained hashing. Security measures include side-channel attack mitigation and cryptographic memory clearing with verification. The paragraph-aware segmentation algorithm preserves semantic coherence across processing boundaries, improving accuracy compared to naive chunking methods. The system supports legal, medical, and financial document processing using domain-optimized BERT variants executing via WebAssembly with optional WebGPU acceleration, all within browser boundaries without server communication.

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