Privacy-Preserving Product-Quantized Approximate Nearest Neighbor Search Framework for Large-scale Datasets via A Hybrid of Fully Homomorphic Encryption and Trusted Execution Environment

Original Abstract

A nearest-neighbor framework is a fundamental tool for various applications involving Large Language Models (LLMs) and Visual Language Models (VLMs). Vectors used for nearest-neighbor searches have richer information for similarity searches. This information leads to security risks, such as embedding inversion and membership attacks. Therefore, Privacy-Preserving Approximate Nearest-Neighbor (PP-ANN) approaches are necessary for highly confidential data. However, conventional PP-ANN approaches based on a Trusted Execution Environment (TEE) or Fully Homomorphic Encryption (FHE) do not achieve practical security or performance. Additionally, conventional approaches focus on the search process rather than database generation for nearest-neighbor. To address these issues, we propose a Privacy-Preserving Product-Quantization Approximate Nearest Neighbor (PPPQ-ANN) framework. PPPQ-ANN provides a multi-layered security structure for vectors based on a hybrid of FHE and TEE. Additionally, PPPQ-ANN minimizes FHE ciphertext computations by combining Product-Quantization (PQ) with optimized data packing. We demonstrate the performance of PPPQ-ANN on million-scale datasets. As a result, PPPQ-ANN achieves database generation in less than 2 hours and more than 50 QPS in a sequential search while preserving privacy. Therefore, PPPQ-ANN optimizes the trade-off between security and performance by utilizing a hybrid of FHE and TEE, achieving practical performance while preserving privacy.