Hybrid privacy-aware semantic search: SVD-truncated document geometry and CKKS-encrypted query reranking under a restricted threat model
arXiv:2606.26373v1 Announce Type: cross Abstract: Dense embeddings power semantic search and retrieval-augmented generation, but embedding-inversion attacks can reconstruct source text from a vector: when a vector database leaks, the documents behind it leak too. The textbook defences are extremes - encrypting the whole search homomorphically is sound but too slow at million-document scale, while privacy noise degrades ranking long before it protects. We study a middle path exploiting the asymmetry between the static collection and the dynamic query. The collection is protected geometrically:
The proliferation of dense embeddings in AI systems, especially semantic search and RAG, has amplified the data privacy risks associated with vector databases, making robust protection methods critical.
Sophisticated readers should care about this as it addresses a fundamental weakness in current AI infrastructure: the privacy vulnerability of embedded data, which could undermine trust and adoption of advanced AI systems.
The proposed hybrid approach offers a practical middle ground for securing large-scale semantic search, potentially enabling privacy-aware AI applications without sacrificing performance as severely as full homomorphic encryption.
- · AI-reliant industries handling sensitive data
- · Cloud service providers
- · Cybersecurity firms
- · Privacy-focused AI developers
- · Malicious actors performing embedding-inversion attacks
- · Companies with poor data governance
- · Unsecured vector database providers
This research provides a more secure architectural pattern for semantic search and retrieval-augmented generation (RAG) systems.
Increased adoption of such privacy-preserving techniques could expand the use of AI in highly regulated sectors like healthcare and finance.
A future where data privacy is baked into the fundamental design of AI systems may lead to more federated, decentralized AI architectures.
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Read at arXiv cs.AI