V3DB：可审计的零知识证明用于验证的向量搜索 over 提交的快照
V3DB: Audit-on-Demand Zero-Knowledge Proofs for Verifiable Vector Search over Committed Snapshots
AI总结：
V3DB通过零知识证明实现可审计的向量搜索，确保在不可信服务提供商执行近邻搜索时的正确性验证。
摘要：
密集检索服务越来越多地成为语义搜索、推荐和检索增强生成的基础，但客户端通常只能获得一个top-k列表，而没有可审计的证据证明其生成过程。我们提出了V3DB，一种可验证的、版本化的向量搜索服务，它允许在需要时进行正确性检查，以验证由可能不可信的服务提供商执行的近邻搜索。V3DB对每个语料库快照进行提交，并将标准的IVF-PQ搜索流程标准化为固定形状、五步查询语义。给定一个公开的快照提交和一个查询嵌入，服务会返回top-k负载，并在被挑战时生成一个简洁的零知识证明，证明输出是执行发布的语义在提交的快照上得到的结果——而无需暴露嵌入语料库或私人索引内容。为了使证明变得实用，V3DB通过将多重集相等性/包含性检查与轻量级边界条件相结合，避免了昂贵的电路内排序和随机访问。我们的基于Plonky2的原型实现实现了比电路-only基线快22倍的证明速度和40%更低的峰值内存消耗，验证时间在毫秒级别。
英文摘要：
Dense retrieval services increasingly underpin semantic search, recommendation, and retrieval-augmented generation, yet clients typically receive only a top-$k$ list with no auditable evidence of how it was produced. We present V3DB, a verifiable, versioned vector-search service that enables audit-on-demand correctness checks for approximate nearest-neighbour (ANN) retrieval executed by a potentially untrusted service provider. V3DB commits to each corpus snapshot and standardises an IVF-PQ search pipeline into a fixed-shape, five-step query semantics. Given a public snapshot commitment and a query embedding, the service returns the top-$k$ payloads and, when challenged, produces a succinct zero-knowledge proof that the output is exactly the result of executing the published semantics on the committed snapshot -- without revealing the embedding corpus or private index contents. To make proving practical, V3DB avoids costly in-circuit sorting and random access by combining multiset equality/inclusion checks with lightweight boundary conditions. Our prototype implementation based on Plonky2 achieves up to $22\times$ faster proving and up to $40\%$ lower peak memory consumption than the circuit-only baseline, with millisecond-level verification time. Github Repo at https://github.com/TabibitoQZP/zk-IVF-PQ.
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