NSHEDB: Noise-Sensitive Homomorphic Encrypted Database Query Engine

2026-02-27

AI Summary

This work addresses the practical challenges of homomorphic encryption in database systems—namely ciphertext expansion, high memory overhead, and costly bootstrapping operations—by proposing a word-level hierarchical homomorphic query engine based on the BFV scheme. For the first time, it enables pure homomorphic evaluation of equality, range, and aggregation queries without requiring cross-scheme re-encryption or trusted hardware. A key innovation is the introduction of a noise-aware query planner that dynamically optimizes circuit depth to enhance performance. Evaluated on the TPC-H benchmark, the system achieves speedups of 20× to 1,370× over existing approaches while reducing storage overhead by up to 73×, all under 128-bit security and with no secret key leakage in the semi-honest model.

Technology Category

• Search and Optimization: Distributed Search
• Machine Learning: Hardware-aware ML
• Data Mining & Knowledge Management: Intelligent Query Processing

Application Category

• Security and Privacy: Large-scale security measurements
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs

Abstract

Homomorphic encryption (HE) enables computations directly on encrypted data, offering strong cryptographic guarantees for secure and privacy-preserving data storage and query execution. However, despite its theoretical power, practical adoption of HE in database systems remains limited due to extreme cipher-text expansion, memory overhead, and the computational cost of bootstrapping, which resets noise levels for correctness. This paper presents NSHEDB, a secure query processing engine designed to address these challenges at the system architecture level. NSHEDB uses word-level leveled HE (LHE) based on the BFV scheme to minimize ciphertext expansion and avoid costly bootstrapping. It introduces novel techniques for executing equality, range, and aggregation operations using purely homomorphic computation, without transciphering between different HE schemes (e.g., CKKS/BFV/TFHE) or relying on trusted hardware. Additionally, it incorporates a noise-aware query planner to extend computation depth while preserving security guarantees. We implement and evaluate NSHEDB on real-world database workloads (TPC-H) and show that it achieves 20x-V1370x speedup and a 73x storage reduction compared to state-of-the-art HE-based systems, while upholding 128-bit security in a semi-honest model with no key release or trusted components.

Problem

• homomorphic encryption
• ciphertext expansion
• bootstrapping
• database query
• privacy-preserving

Innovation

• Homomorphic Encryption
• Noise-Aware Query Planning
• Leveled HE
• Ciphertext Expansion Reduction
• Secure Database Query