Retrieval and Privacy of Human Digital Context using AI Engine
Year: 2026
Type: preprint
Abstract: EchoSphere is a privacy-first, local-first AI memory system designed to function as a lifelong digital knowledge assistant. The system captures user activities including web browsing, documents, notes, and conversations, storing them securely in a local vector database while leveraging offline Large Language Models (LLMs) for semantic retrieval. Unlike traditional cloud-based assistants, EchoSphere ensures complete user data ownership by processing all information locally without mandatory cloud storage. The proposed architecture integrates semantic embeddings, vector databases, local AI inference, and encrypted selective sharing to provide secure, context-aware memory retrieval. Experimental evaluation demonstrates high retrieval accuracy, efficient storage utilization, low query latency, and complete privacy preservation. The framework provides a scalable and privacy-preserving approach to digital memory augmentation for applications in education, healthcare, research, and personal knowledge management.
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7166526057
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