ANNProof: Building a verifiable and efficient outsourced approximate nearest neighbor search system on blockchain

Abstract

Data-as-a-service is increasingly prevalent, with outsourced K-approximate nearest neighbors search (K-ANNS) gaining popularity in applications like similar image retrieval and anti-money laundering. However, malicious search service providers and dataset providers in current outsourced query systems cause incorrect user query results. To address this, we propose ANNProof, a novel framework supporting verifiable outsourced K-ANNS on the blockchain. ANNProof utilizes two innovative authenticated data structures (ADS), the Merkle HNSW node tree, and the Merkle vector identifier tree, for efficient K-ANNS query verification. Additionally, we employ the Merkle sharding tree as an ADS optimization technique, reducing the overhead of delivering verifiable queries. We implement the ADS construction protocol based on blockchain smart contracts to ensure tamper-evident datasets and enhance execution efficiency via a contract state consistency checking scheme. Extensive evaluations show that ANNProof reduces VO generation time, result verification time, and VO size by 160, 120, and 28×, respectively, compared to the state-of-the-art systems. Moreover, ADS construction using ANNProof takes at most 2% of the index construction time, resulting in a negligible overhead for implementing verifiable queries. Meanwhile, the sharding optimization accelerates ADS updates by 53×.