Enc2DB: A Hybrid and Adaptive Encrypted Query Processing Framework

• URL: http://arxiv.org/abs/2404.06819v1
• Date: Wed, 10 Apr 2024 08:11:12 GMT
• Title: Enc2DB: A Hybrid and Adaptive Encrypted Query Processing Framework
• Authors: Hui Li, Jingwen Shi, Qi Tian, Zheng Li, Yan Fu, Bingqing Shen, Yaofeng Tu,
• Abstract summary: 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.
• Score: 47.11111145443189
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As cloud computing gains traction, data owners are outsourcing their data to cloud service providers (CSPs) for Database Service (DBaaS), bringing in a deviation of data ownership and usage, and intensifying privacy concerns, especially with potential breaches by hackers or CSP insiders. To address that, encrypted database services propose encrypting every tuple and query statement before submitting to the CSP, ensuring data confidentiality when the CSP is honest-but-curious, or even compromised. Existing solutions either employ property preserving cryptography schemes, which can perform certain operations over ciphertext without decrypting the data over the CSP, or utilize trusted execution environment (TEE) to safeguard data and computations from the CSP. Based on these efforts, we introduce Enc2DB, a novel secure database system, following a hybrid strategy on PostgreSQL and openGauss. We present a micro-benchmarking test and self-adaptive mode switch strategy that can dynamically choose the best execution path (cryptography or TEE) to answer a given query. Besides, we also design and implement a ciphertext index compatible with native cost model and query optimizers to accelerate query processing. Empirical study over TPC-C test justifies that Enc2DB outperforms pure TEE and cryptography solutions, and our ciphertext index implementation also outperforms the state-of-the-art cryptographic-based system.

Related papers

• HOPE: Homomorphic Order-Preserving Encryption for Outsourced Databases -- A Stateless Approach [1.1701842638497677]
  Homomorphic OPE (HOPE) is a new OPE scheme that eliminates client-side storage and avoids additional client-server interaction during query execution. 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)
• DataSeal: Ensuring the Verifiability of Private Computation on Encrypted Data [14.21750921409931]
  We introduce DataSeal, which combines the low overhead of the algorithm-based fault tolerance (ABFT) technique with the confidentiality of Fully Homomorphic Encryption (FHE) DataSeal achieves much lower overheads for providing computation verifiability for FHE than other techniques that include MAC, ZKP, and TEE.
  arXiv  Detail & Related papers  (2024-10-19T21:19:39Z)
• Secure Outsourced Decryption for FHE-based Privacy-preserving Cloud Computing [3.125865379632205]
  Homomorphic encryption (HE) is one solution for safeguarding data privacy, enabling encrypted data to be processed securely in the cloud. We propose an outsourced decryption protocol for the prevailing RLWE-based fully homomorphic encryption schemes. Our experiments demonstrate that the proposed protocol achieves up to a $67%$ acceleration in the client's local decryption, accompanied by a $50%$ reduction in space usage.
  arXiv  Detail & Related papers  (2024-06-28T14:51:36Z)
• 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)
• A Survey on Property-Preserving Database Encryption Techniques in the Cloud [0.0]
  There are concerns about the security and confidentiality of the outsourced data. The report at hand presents a survey on common encryption techniques used for storing data in relation Cloud database services.
  arXiv  Detail & Related papers  (2023-12-19T11:50:31Z)
• DiCE -- A Data Encryption Proxy for the Cloud [0.0]
  There are concerns about the confidentiality and security of the outsourced data. The DiCE' driver parses queries as a proxy and encrypts these queries. This allows to execute many queries on an encrypted database in the cloud with the relational performance as on unencrypted databases.
  arXiv  Detail & Related papers  (2023-10-09T13:33:59Z)
• 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)
• A Survey of Secure Computation Using Trusted Execution Environments [80.58996305474842]
  This article provides a systematic review and comparison of TEE-based secure computation protocols. We first propose a taxonomy that classifies secure computation protocols into three major categories, namely secure outsourced computation, secure distributed computation and secure multi-party computation. Based on these criteria, we review, discuss and compare the state-of-the-art TEE-based secure computation protocols for both general-purpose computation functions and special-purpose ones.
  arXiv  Detail & Related papers  (2023-02-23T16:33:56Z)
• 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.