Vector DB and ANN vs PHE conflict, is there a practical workaround? [D]
Our take
The intersection of vector databases, approximate nearest neighbor (ANN) search, and privacy-preserving techniques such as Partially Homomorphic Encryption (PHE) presents a compelling yet complex challenge for data management. As highlighted in a recent community discussion, the efficiency of vector databases is compromised when we attempt to integrate PHE, leading to a significant trade-off between speed and security. This dilemma is particularly poignant given the increasing emphasis on data privacy and the growing need for fast retrieval systems that can handle vast amounts of data. For context, similar topics have emerged in our discussions, such as in "Job has me doing a needlessly complicated task" and "Build AI Financial Models in Sourcetable", where the effectiveness of tools in managing complex tasks is necessary for enhancing productivity.
The primary challenge arises when encrypted embeddings force a linear scan or exact computation, effectively rendering many ANN techniques ineffective. This situation is particularly concerning for organizations that rely on rapid similarity searches across large datasets. The proposed workaround of abandoning vector databases in favor of standard databases, storing embeddings as BLOBs, and employing metadata filtering techniques like RFID or tag-based systems is innovative. However, it raises critical questions about scalability and efficiency. Will such a method truly outperform traditional ANN approaches when faced with millions of embeddings? Moreover, as organizations seek to balance privacy with performance, the need for a robust and efficient solution becomes paramount.
As we navigate this landscape, it is essential to consider whether hybrid approaches might provide a viable path forward. Techniques such as secure enclaves, partial decryption, or tiered search mechanisms could potentially allow for a more nuanced integration of encrypted embeddings with ANN. The community's insights into these hybrid strategies could illuminate practical solutions that are both privacy-preserving and efficient. Additionally, investigating existing real-world systems that successfully implement privacy-preserving vector searches could offer invaluable lessons and inspire confidence in new methodologies.
Looking ahead, the challenge remains not only to find a viable technical solution but also to ensure that these approaches are accessible and scalable for a broad range of users. The focus should be on fostering a user-centric perspective that prioritizes practical outcomes over technical complexity. As we explore these innovative solutions, it is crucial to ask: How can we implement privacy-preserving measures without sacrificing the performance that users have come to expect? The answers to these questions will undoubtedly shape the future of data management, making it an exciting space to watch.
Hey everyone,
I have been digging into vector databases, ANN search, and privacy preserving techniques (specifically PHE), and I have hit a design roadblock that I would love some input on.
The problem:
Using a vector DB with ANN (HNSW, IVF, etc.) is great for fast similarity search at scale.
But if we introduce Partially Homomorphic Encryption (PHE), we lose the ability to efficiently use ANN.
This happens because encrypted embeddings force us into linear scan or exact computation, which makes ANN useless.
What I am considering:
One workaround I thought of is to drop the vector DB entirely, store embeddings in a standard database as BLOBs, and use something like RFID or tag based filtering to narrow down candidates before computing similarity.
The idea is to reduce the search space first using metadata, then run similarity on a much smaller subset.
Concerns:
Will this scale to millions of embeddings?
Is database retrieval and filtering actually faster than ANN in practice?
Am I just reinventing a worse version of a vector database?
Questions for the community:
- Is there a practical way to combine ANN with encrypted embeddings?
- Are there hybrid approaches like secure enclaves, partial decryption, or tiered search that actually work in production?
- Would a metadata first filtering pipeline (RFID or tags to subset to similarity) scale better than I think?
- Are there any real world systems doing privacy preserving vector search at scale?
Context:
Potential scale is around 1 million plus embeddings.
Priority is balancing privacy and performance.
Use case is fast retrieval with secure storage of embeddings.
Would really appreciate any insights, papers, or architecture suggestions.
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