PP-EDUVec: Privacy-Preserving Intelligent Management Algorithms for Educational-Corpus Vector Databases Under Retrieval-Augmented Learning

Educational platforms increasingly rely on vector databases to store and retrieve embedding representations of large-scale learning corpora (e.g., lecture notes, assignments, feedback, and student Q&A) for retrieval-augmented generation and analytics. However, directly indexing educational text embeddings raises privacy risks (student identities, sensitive performance signals, and protected attributes) and creates a management challenge: embeddings drift as curricula evolve, access policies change, and new content arrives continuously. This paper studies privacy-preserving intelligent management of educational-corpus vector libraries and proposes a novel, end-to-end algorithmic framework that jointly optimizes (i) privacy leakage control, (ii) retrieval quality, and (iii) operational efficiency under streaming updates. We introduce a hierarchical policy-aware vector lifecycle model, a privacy budget scheduler for adaptive re-embedding and re-indexing, and a secure-aware clustering-and-routing mechanism that supports fast query-time filtering with minimal accuracy loss. The resulting system, PP-EDUVec, enables compliant similarity search across multi-tenant educational data while automatically maintaining index health (freshness, redundancy, and utility) over time. On the EDU-Mix benchmark, PP-EDUVec achieves Recall@10 =0.835 while reducing representation leakage (LeakRep) from 0.215 to 0.136 (−36.7%) and access-pattern leakage (LeakAP) from 0.398 to 0.255 (−35.9%), and lowering mean latency from 42.1 ms to 33.4 ms (−20.7%) and weekly maintenance time from 55.0 to 35.8 min/week (−34.9%) compared with PostFilter.