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

2025-12-03

AI Summary

To address privacy risks arising from access pattern leakage in encrypted range queries under cloud environments, this paper proposes a novel scheme that jointly achieves strong security guarantees and high efficiency. Methodologically, it introduces, for the first time, a learnable spatial index into encrypted settings, integrating Paillier homomorphic encryption with a hierarchical prediction architecture. It further designs a noise-injected bucket mechanism and a permutation-based secure bucket prediction protocol, augmented by a secure point extraction protocol, to simultaneously protect data confidentiality, query content, and access patterns. Experimental evaluation on both real-world and synthetic datasets demonstrates that the proposed scheme significantly outperforms state-of-the-art approaches in query latency and throughput, while providing rigorous formal security proofs under standard cryptographic assumptions.

Technology Category

• Machine Learning: Privacy
• Search and Optimization: Learning to Search
• Data Mining & Knowledge Management: Intelligent Query Processing

Application Category

• Security and Privacy: Applications of cryptography
• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

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.

Problem

• Develops a secure learned spatial index for encrypted data
• Enables efficient privacy-preserving range queries with strong security
• Obfuscates query execution paths to prevent access pattern leakage

Innovation

• Secure learned spatial index with Paillier cryptosystem
• Permutation-based secure bucket prediction protocol
• Secure point extraction protocol for candidate results