NSHEDB: Noise-Sensitive Homomorphic Encrypted Database Query Engine

• URL: http://arxiv.org/abs/2602.24271v1
• Date: Fri, 27 Feb 2026 18:41:10 GMT
• Title: NSHEDB: Noise-Sensitive Homomorphic Encrypted Database Query Engine
• Authors: Boram Jung, Yuliang Li, Hung-Wei Tseng,
• Abstract summary: Homomorphic encryption (HE) enables computations directly on encrypted data, offering strong cryptographic guarantees for secure and privacy-preserving data storage and query execution.<n>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.<n>NSHEDB is a secure query processing engine designed to address these challenges at the system architecture level.
• Score: 5.1414137771359565
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

Related papers

• A Fuzzy Logic-Based Cryptographic Framework For Real-Time Dynamic Key Generation For Enhanced Data Encryption [0.24629531282150874]
  Brute-force attacks, key compromise, and unauthorized access have become highly common cyber threats.<n>This research presents a novel fuzzy logic-based cryptographic framework that dynamically generates encryption keys in real-time.
  arXiv  Detail & Related papers  (2025-11-18T04:34:31Z)
• FHE-SQL: Fully Homomorphic Encrypted SQL Database [0.09176056742068811]
  FHE- is a privacy-preserving database system that enables secure query processing on encrypted data.<n>Unlike property-preserving systems such as CryptDB, FHE- performs computations entirely under encryption.
  arXiv  Detail & Related papers  (2025-10-17T08:07:27Z)
• Hades: Homomorphic Augmented Decryption for Efficient Symbol-comparison -- A Database's Perspective [1.3824176915623292]
  This paper introduces HADES, a novel cryptographic framework that enables efficient and secure comparisons on encrypted data.<n>Based on the Ring Learning with Errors (RLWE) problem, HADES provides CPA-security and incorporates perturbation-aware encryption to mitigate frequency-analysis attacks.
  arXiv  Detail & Related papers  (2024-12-28T02:47:14Z)
• Nemesis: Noise-randomized Encryption with Modular Efficiency and Secure Integration in Machine Learning Systems [1.3824176915623292]
  Nemesis is a framework that accelerates FHE-based machine learning systems without compromising accuracy or security.<n>We prove the security of Nemesis under standard cryptographic assumptions.<n>Results show that Nemesis significantly reduces the computational overhead of FHE-based ML systems.
  arXiv  Detail & Related papers  (2024-12-18T22:52:12Z)
• 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)
• 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)
• Coding-Based Hybrid Post-Quantum Cryptosystem for Non-Uniform Information [53.85237314348328]
  We introduce for non-uniform messages a novel hybrid universal network coding cryptosystem (NU-HUNCC) We show that NU-HUNCC is information-theoretic individually secured against an eavesdropper with access to any subset of the links.
  arXiv  Detail & Related papers  (2024-02-13T12:12:39Z)
• SOCI^+: An Enhanced Toolkit for Secure OutsourcedComputation on Integers [50.608828039206365]
  We propose SOCI+ which significantly improves the performance of SOCI. SOCI+ employs a novel (2, 2)-threshold Paillier cryptosystem with fast encryption and decryption as its cryptographic primitive. Compared with SOCI, our experimental evaluation shows that SOCI+ is up to 5.4 times more efficient in computation and 40% less in communication overhead.
  arXiv  Detail & Related papers  (2023-09-27T05:19:32Z)
• THE-X: Privacy-Preserving Transformer Inference with Homomorphic Encryption [112.02441503951297]
  Privacy-preserving inference of transformer models is on the demand of cloud service users. We introduce $textitTHE-X$, an approximation approach for transformers, which enables privacy-preserving inference of pre-trained models.
  arXiv  Detail & Related papers  (2022-06-01T03:49:18Z)
• Recovering AES Keys with a Deep Cold Boot Attack [91.22679787578438]
  Cold boot attacks inspect the corrupted random access memory soon after the power has been shut down. In this work, we combine a novel cryptographic variant of a deep error correcting code technique with a modified SAT solver scheme to apply the attack on AES keys. Our results show that our methods outperform the state of the art attack methods by a very large margin.
  arXiv  Detail & Related papers  (2021-06-09T07:57:01Z)
• Faster Secure Data Mining via Distributed Homomorphic Encryption [108.77460689459247]
  Homomorphic Encryption (HE) is receiving more and more attention recently for its capability to do computations over the encrypted field. We propose a novel general distributed HE-based data mining framework towards one step of solving the scaling problem. We verify the efficiency and effectiveness of our new framework by testing over various data mining algorithms and benchmark data-sets.
  arXiv  Detail & Related papers  (2020-06-17T18:14:30Z)

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.