Towards Privacy-Preserving Range Queries with Secure Learned Spatial Index over Encrypted Data

• URL: http://arxiv.org/abs/2512.03669v1
• Date: Wed, 03 Dec 2025 10:59:40 GMT
• Title: Towards Privacy-Preserving Range Queries with Secure Learned Spatial Index over Encrypted Data
• Authors: Zuan Wang, Juntao Lu, Jiazhuang Wu, Youliang Tian, Wei Song, Qiuxian Li, Duo Zhang,
• Abstract summary: We propose a novel privacy-preserving range query scheme over encrypted datasets.<n>SLS-INDEX integrates the Paillier cryptosystem with a hierarchical prediction architecture and noise-injected buckets.<n> SLRQ significantly outperforms existing solutions in query efficiency while ensuring dataset, query, result, and access pattern privacy.
• Score: 8.495233108444202
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: With the growing reliance on cloud services for large-scale data management, preserving the security and privacy of outsourced datasets has become increasingly critical. While encrypting data and queries can prevent direct content exposure, recent research reveals that adversaries can still infer sensitive information via access pattern and search path analysis. However, existing solutions that offer strong access pattern privacy often incur substantial performance overhead. In this paper, we propose a novel privacy-preserving range query scheme over encrypted datasets, offering strong security guarantees while maintaining high efficiency. To achieve this, we develop secure learned spatial index (SLS-INDEX), a secure learned index that integrates the Paillier cryptosystem with a hierarchical prediction architecture and noise-injected buckets, enabling data-aware query acceleration in the encrypted domain. To further obfuscate query execution paths, SLS-INDEXbased Range Queries (SLRQ) employs a permutation-based secure bucket prediction protocol. Additionally, we introduce a secure point extraction protocol that generates candidate results to reduce the overhead of secure computation. We provide formal security analysis under realistic leakage functions and implement a prototype to evaluate its practical performance. Extensive experiments on both real-world and synthetic datasets demonstrate that SLRQ significantly outperforms existing solutions in query efficiency while ensuring dataset, query, result, and access pattern privacy.

Related papers

• Efficient Privacy-Preserving Retrieval Augmented Generation with Distance-Preserving Encryption [25.87368479678027]
  RAG has emerged as a key technique for enhancing response quality of LLMs without high computational cost.<n>In traditional architectures, RAG services are provided by a single entity that hosts the dataset within a trusted local environment.<n>This dependence on untrusted third-party services introduces privacy risks.<n>We propose an efficient privacy-preserving RAG framework (ppRAG) tailored for untrusted cloud environments.
  arXiv  Detail & Related papers  (2026-01-18T09:29:50Z)
• Multiparty Authorization for Secure Data Storage in Cloud Environments using Improved Attribute-Based Encryption [0.0]
  Attribute-Based Encryption (ABE) is one of the potential security and access control techniques for secure data storage and authorization.<n>The proposed work divides into two objectives: (i) provide access to authorized users and (ii) secure data storage in a cloud environment.<n>The security and performance analysis results demonstrate that the proposed scheme is more robust and secure.
  arXiv  Detail & Related papers  (2025-12-29T05:41:08Z)
• Secure and Scalable Face Retrieval via Cancelable Product Quantization [48.58859061524146]
  homomorphic encryption (HE) offers strong security guarantees by enabling arithmetic computations in the cipher space.<n>We propose Cancelable Product Quantization, a highly efficient framework for secure face representation retrieval.
  arXiv  Detail & Related papers  (2025-08-31T10:19:55Z)
• Enhancing Leakage Attacks on Searchable Symmetric Encryption Using LLM-Based Synthetic Data Generation [0.0]
  Searchable Symmetric Encryption (SSE) enables efficient search capabilities over encrypted data, allowing users to maintain privacy while utilizing cloud storage.<n>SSE schemes are vulnerable to leakage attacks that exploit access patterns, search frequency, and volume information.<n>We propose a novel approach that leverages large language models (LLMs), specifically GPT-4 variants, to generate synthetic documents that statistically and semantically resemble the real-world dataset of Enron emails.
  arXiv  Detail & Related papers  (2025-04-29T04:23:10Z)
• How Robust Are Router-LLMs? Analysis of the Fragility of LLM Routing Capabilities [62.474732677086855]
  Large language model (LLM) routing has emerged as a crucial strategy for balancing computational costs with performance.<n>We propose the DSC benchmark: Diverse, Simple, and Categorized, an evaluation framework that categorizes router performance across a broad spectrum of query types.
  arXiv  Detail & Related papers  (2025-03-20T19:52:30Z)
• HOPE: Homomorphic Order-Preserving Encryption for Outsourced Databases -- A Stateless Approach [0.7770535067924795]
  Homomorphic OPE (HOPE) is a new OPE scheme that eliminates client-side storage and avoids additional client-server interaction during query execution.<n>We provide a formal cryptographic analysis of HOPE, proving its security under the widely accepted IND-OCPA model.
  arXiv  Detail & Related papers  (2024-11-26T00:38:46Z)
• FRAG: Toward Federated Vector Database Management for Collaborative and Secure Retrieval-Augmented Generation [1.3824176915623292]
  This paper introduces textitFederated Retrieval-Augmented Generation (FRAG), a novel database management paradigm tailored for the growing needs of retrieval-augmented generation (RAG) systems. FRAG enables mutually-distrusted parties to collaboratively perform Approximate $k$-Nearest Neighbor (ANN) searches on encrypted query vectors and encrypted data stored in distributed vector databases.
  arXiv  Detail & Related papers  (2024-10-17T06:57:29Z)
• Robust Utility-Preserving Text Anonymization Based on Large Language Models [80.5266278002083]
  Anonymizing text that contains sensitive information is crucial for a wide range of applications.<n>Existing techniques face the emerging challenges of the re-identification ability of large language models.<n>We propose a framework composed of three key components: a privacy evaluator, a utility evaluator, and an optimization component.
  arXiv  Detail & Related papers  (2024-07-16T14:28:56Z)
• Enc2DB: A Hybrid and Adaptive Encrypted Query Processing Framework [47.11111145443189]
  We introduce Enc2DB, a novel secure database system following a hybrid strategy on and openGauss. We present a micro-benchmarking test and self-adaptive mode switch strategy that can choose the best execution path (cryptography or TEE) to answer a given query. We also design and implement a ciphertext index compatible with native cost model and querys to accelerate query processing.
  arXiv  Detail & Related papers  (2024-04-10T08:11:12Z)
• Reinforcement Learning on Encrypted Data [58.39270571778521]
  We present a preliminary, experimental study of how a DQN agent trained on encrypted states performs in environments with discrete and continuous state spaces. Our results highlight that the agent is still capable of learning in small state spaces even in presence of non-deterministic encryption, but performance collapses in more complex environments.
  arXiv  Detail & Related papers  (2021-09-16T21:59:37Z)
• CryptoSPN: Privacy-preserving Sum-Product Network Inference [84.88362774693914]
  We present a framework for privacy-preserving inference of sum-product networks (SPNs) CryptoSPN achieves highly efficient and accurate inference in the order of seconds for medium-sized SPNs.
  arXiv  Detail & Related papers  (2020-02-03T14:49:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.