zk-Oracle: trusted off-chain compute and storage for decentralized applications

IF 1.5 4区计算机科学 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS Distributed and Parallel Databases Pub Date : 2024-08-28 DOI:10.1007/s10619-024-07444-6

Binbin Gu, Faisal Nawab

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Abstract

Blockchain and Decentralized Applications (DApps) are increasingly important for creating trust and transparency in data storage and computation. However, on-chain transactions are often costly and slow. To overcome this challenge, off-chain nodes can be used to store and compute data. Unfortunately, this introduces the risk of untrusted nodes. To address this, authenticated data structures have been proposed, however, this ignores the compute of data from the raw data. We tackle this challenge by introducing zk-Oracle, which provides an efficient and trusted compute and storage off-chain. There is a challenge in using zero-knowledge proofs (zk-proof for short), which is the large proof generation time. We aim to overcome it with novel designs in zk-Oracle. zk-Oracle builds on zk-proofs technologies to achieve two goals. First, the computation of data structures from raw data and the corresponding proof generation is improved in terms of performance. Second, the verification on-chain is inexpensive and fast. Our experiments show that we can speed up zk-proof generation by up to \(550 \times \) faster than the baseline method.

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zk-Oracle：面向分散式应用的可信链外计算和存储

区块链和去中心化应用程序（DApps）对于在数据存储和计算中建立信任和透明度越来越重要。然而，链上交易通常成本高、速度慢。为了克服这一难题，可以使用链外节点来存储和计算数据。遗憾的是，这会带来节点不受信任的风险。为了解决这个问题，有人提出了认证数据结构，但这忽略了从原始数据中计算数据。我们通过引入zk-Oracle来应对这一挑战，zk-Oracle可提供高效、可信的链外计算和存储。使用零知识证明（简称 zk-proof）存在一个挑战，即证明生成时间较长。我们的目标是通过 zk-Oracle 中的新颖设计来克服这一难题。zk-Oracle 基于 zk-proofs 技术，以实现两个目标。首先，从原始数据计算数据结构和生成相应证明的性能得到了提高。其次，链上验证成本低、速度快。我们的实验表明，与基线方法相比，我们可以将zk-proof的生成速度提高达\(550 \times\)。

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来源期刊

Distributed and Parallel Databases 工程技术-计算机：理论方法

CiteScore

3.50

自引率

0.00%

发文量

审稿时长

>12 weeks

期刊介绍： Distributed and Parallel Databases publishes papers in all the traditional as well as most emerging areas of database research, including: Availability and reliability; Benchmarking and performance evaluation, and tuning; Big Data Storage and Processing; Cloud Computing and Database-as-a-Service; Crowdsourcing; Data curation, annotation and provenance; Data integration, metadata Management, and interoperability; Data models, semantics, query languages; Data mining and knowledge discovery; Data privacy, security, trust; Data provenance, workflows, Scientific Data Management; Data visualization and interactive data exploration; Data warehousing, OLAP, Analytics; Graph data management, RDF, social networks; Information Extraction and Data Cleaning; Middleware and Workflow Management; Modern Hardware and In-Memory Database Systems; Query Processing and Optimization; Semantic Web and open data; Social Networks; Storage, indexing, and physical database design; Streams, sensor networks, and complex event processing; Strings, Texts, and Keyword Search; Spatial, temporal, and spatio-temporal databases; Transaction processing; Uncertain, probabilistic, and approximate databases.