Confidential Databases Without Cryptographic Mappings

• URL: http://arxiv.org/abs/2603.18836v2
• Date: Mon, 23 Mar 2026 11:26:09 GMT
• Title: Confidential Databases Without Cryptographic Mappings
• Abstract summary: FEDB is a novel CDB design that removes cryptographic operations from the critical path.<n>This paradigm shift reduces the runtime overhead by up to 78.0 times on industry-standard benchmarks.
• Score: 5.019292555403337
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Confidential databases (CDBs) are essential for enabling secure queries over sensitive data in untrusted cloud environments using confidential computing hardware. While adoption is growing, widespread deployment is hindered by high performance overhead from frequent synchronous cryptographic operations, which causes significant computational and memory bottlenecks. We present FEDB, a novel CDB design that removes cryptographic operations from the critical path. FEDB leverages crypto-free mappings, which maintain data-independent identifiers within the database while securely mapping them to plaintext secrets in a trusted domain. This paradigm shift reduces the runtime overhead by up to 78.0 times on industry-standard benchmarks including TPC-C and TPC-H.

Related papers

• Training-Free Hashing-Based Attention via Binary Principal Components [62.88957443238231]
  Long-context large language models (LLMs) are increasingly deployed in real-world applications.<n>Existing sparse attention reduce computation by attending to fewer KV pairs, but often suffer from substantial accuracy degradation.<n>We present BinaryPC, a training-free, data-aware hashing-based sparse attention for long-context LLMs.
  arXiv  Detail & Related papers  (2026-08-05T03:19:21Z)
• SecPI: Secure Code Generation with Reasoning Models via Security Reasoning Internalization [50.71047638695205]
  Reasoning language models (RLMs) are increasingly used in programming.<n>Yet, even state-of-the-art RLMs frequently introduce critical security vulnerabilities in generated code.<n>We present SecPI, a fine-tuning pipeline that teaches RLMs to internalize structured security reasoning.
  arXiv  Detail & Related papers  (2026-04-04T04:29:11Z)
• NSHEDB: Noise-Sensitive Homomorphic Encrypted Database Query Engine [5.1414137771359565]
  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.
  arXiv  Detail & Related papers  (2026-02-27T18:41:10Z)
• 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)
• TEE-based Key-Value Stores: a Survey [1.1060425537315088]
  Key-Value Stores (KVSs) store data as key-value pairs and have gained popularity due to their simplicity, scalability, and fast retrieval capabilities.<n> storing sensitive data in KVSs requires strong security properties to prevent data leakage and unauthorized tampering.<n>This paper examines the state of the art in TEE-based confidential KVSs and highlights common design strategies used in KVSs to leverage TEE security features.
  arXiv  Detail & Related papers  (2025-01-06T16:26:44Z)
• GNN-Based Code Annotation Logic for Establishing Security Boundaries in C Code [41.10157750103835]
  Securing sensitive operations in today's interconnected software landscape is crucial yet challenging. Modern platforms rely on Trusted Execution Environments (TEEs) to isolate security sensitive code from the main system. Code Logic (CAL) is a pioneering tool that automatically identifies security sensitive components for TEE isolation.
  arXiv  Detail & Related papers  (2024-11-18T13:40:03Z)
• BlindexTEE: A Blind Index Approach towards TEE-supported End-to-end Encrypted DBMS [1.7856481599200345]
  We present BlindexTEE, a new component that sits between the application business-logic and the database. BlindexTEE is shielded from malicious users or compromised environments by executing inside an SEV-SNP confidential VM. By encrypting and re-encrypting data, it builds blind indices, used later on to efficiently query the indices.
  arXiv  Detail & Related papers  (2024-11-04T13:45:28Z)
• 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)
• EmojiPrompt: Generative Prompt Obfuscation for Privacy-Preserving Communication with Cloud-based LLMs [34.77734655124251]
  EmojiPrompt performs generative transformation, obfuscating private data within prompts with linguistic and non-linguistic elements.<n>We evaluate EmojiPrompt's performance across 8 datasets from various domains.<n>EmojiPrompt's atomic-level obfuscation allows it to function exclusively with cloud-based LLMs.
  arXiv  Detail & Related papers  (2024-02-08T17:57:11Z)
• HasTEE+ : Confidential Cloud Computing and Analytics with Haskell [50.994023665559496]
  Confidential computing enables the protection of confidential code and data in a co-tenanted cloud deployment using specialized hardware isolation units called Trusted Execution Environments (TEEs) TEEs offer low-level C/C++-based toolchains that are susceptible to inherent memory safety vulnerabilities and lack language constructs to monitor explicit and implicit information-flow leaks. We address the above with HasTEE+, a domain-specific language (cla) embedded in Haskell that enables programming TEEs in a high-level language with strong type-safety.
  arXiv  Detail & Related papers  (2024-01-17T00:56:23Z)
• 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.