Threshold-Protected Searchable Sharing: Privacy Preserving Aggregated-ANN Search for Collaborative RAG (2507.17199v1)

Abstract: LLM-powered search services have driven data integration as a significant trend. However, this trend's progress is fundamentally hindered, despite the fact that combining individual knowledge can significantly improve the relevance and quality of responses in specialized queries and make AI more professional at providing services. Two key bottlenecks are private data repositories' locality constraints and the need to maintain compatibility with mainstream search techniques, particularly Hierarchical Navigable Small World (HNSW) indexing for high-dimensional vector spaces. In this work, we develop a secure and privacy-preserving aggregated approximate nearest neighbor search (SP-A$^2$NN) with HNSW compatibility under a threshold-based searchable sharing primitive. A sharable bitgraph structure is constructed and extended to support searches and dynamical insertions over shared data without compromising the underlying graph topology. The approach reduces the complexity of a search from $O(n^2)$ to $O(n)$ compared to naive (undirected) graph-sharing approach when organizing graphs in the identical HNSW manner. On the theoretical front, we explore a novel security analytical framework that incorporates privacy analysis via reductions. The proposed leakage-guessing proof system is built upon an entirely different interactive game that is independent of existing coin-toss game design. Rather than being purely theoretical, this system is rooted in existing proof systems but goes beyond them to specifically address leakage concerns and standardize leakage analysis -- one of the most critical security challenges with AI's rapid development.