Privacy-Preserving Product-Quantized Approximate Nearest Neighbor Search Framework for Large-scale Datasets via A Hybrid of Fully Homomorphic Encryption and Trusted Execution Environment
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866918455305830400 |
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| author | Saeki, Shozo Kawahara, Minoru Aman, Hirohisa |
| author_facet | Saeki, Shozo Kawahara, Minoru Aman, Hirohisa |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_17816 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Privacy-Preserving Product-Quantized Approximate Nearest Neighbor Search Framework for Large-scale Datasets via A Hybrid of Fully Homomorphic Encryption and Trusted Execution Environment Saeki, Shozo Kawahara, Minoru Aman, Hirohisa Cryptography and Security 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. |
| title | Privacy-Preserving Product-Quantized Approximate Nearest Neighbor Search Framework for Large-scale Datasets via A Hybrid of Fully Homomorphic Encryption and Trusted Execution Environment |
| topic | Cryptography and Security |
| url | https://arxiv.org/abs/2604.17816 |