TELEX: Two-Level Learned Index for Rich Queries on Enclave-Based Blockchain Systems

IF 8.9 2区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

IEEE Transactions on Knowledge and Data Engineering Pub Date : 2025-04-28 DOI:10.1109/TKDE.2025.3564905

Haotian Wu;Yuzhe Tang;Zhaoyan Shen;Jun Tao;Chenhao Lin;Zhe Peng

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引用次数: 0

Abstract

Blockchain has become a popular paradigm for secure and immutable data storage. Despite its numerous applications across various fields, concerns regarding the user privacy and result integrity during data queries persist. Additionally, the need for rich query functionalities to harness the full potential of blockchain data remains an area ripe for exploration. In order to address these challenges, our paper first utilizes a framework based on the Trusted Execution Environment (TEE) and oblivious RAM technique to achieve both privacy and data integrity. To enhance the query efficiency over the entire blockchain, we then devise a two-level learned indexing methodology named TELEX within the TEE for both integer and string keys. We also propose different query processing algorithms for versatile query types, including exact queries, aggregate queries, Boolean queries, and range queries. By implementing the prototype and conducting extensive evaluation, we demonstrate the feasibility and remarkable improvement in efficiency compared to existing solutions.

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基于enclave的区块链系统中富查询的两级学习索引

区块链已经成为安全且不可变的数据存储的流行范例。尽管它在各个领域有许多应用程序，但在数据查询期间对用户隐私和结果完整性的关注仍然存在。此外，需要丰富的查询功能来利用区块链数据的全部潜力，这仍然是一个有待探索的领域。为了应对这些挑战，本文首先利用基于可信执行环境（TEE）和遗忘RAM技术的框架来实现隐私和数据完整性。为了提高整个区块链的查询效率，我们在TEE中为整数键和字符串键设计了一种名为TELEX的两级学习索引方法。我们还针对多种查询类型提出了不同的查询处理算法，包括精确查询、聚合查询、布尔查询和范围查询。通过实现原型并进行广泛的评估，我们证明了与现有解决方案相比，该方案的可行性和效率的显著提高。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Transactions on Knowledge and Data Engineering 工程技术-工程：电子与电气

CiteScore

11.70

自引率

3.40%

发文量

515

审稿时长

6 months

期刊介绍： The IEEE Transactions on Knowledge and Data Engineering encompasses knowledge and data engineering aspects within computer science, artificial intelligence, electrical engineering, computer engineering, and related fields. It provides an interdisciplinary platform for disseminating new developments in knowledge and data engineering and explores the practicality of these concepts in both hardware and software. Specific areas covered include knowledge-based and expert systems, AI techniques for knowledge and data management, tools, and methodologies, distributed processing, real-time systems, architectures, data management practices, database design, query languages, security, fault tolerance, statistical databases, algorithms, performance evaluation, and applications.