{"title":"zk-Oracle：面向分散式应用的可信链外计算和存储","authors":"Binbin Gu, Faisal Nawab","doi":"10.1007/s10619-024-07444-6","DOIUrl":null,"url":null,"abstract":"<p>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 <span>\\(550 \\times \\)</span> faster than the baseline method.</p>","PeriodicalId":50568,"journal":{"name":"Distributed and Parallel Databases","volume":null,"pages":null},"PeriodicalIF":1.5000,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"zk-Oracle: trusted off-chain compute and storage for decentralized applications\",\"authors\":\"Binbin Gu, Faisal Nawab\",\"doi\":\"10.1007/s10619-024-07444-6\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>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 <span>\\\\(550 \\\\times \\\\)</span> faster than the baseline method.</p>\",\"PeriodicalId\":50568,\"journal\":{\"name\":\"Distributed and Parallel Databases\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.5000,\"publicationDate\":\"2024-08-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Distributed and Parallel Databases\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.1007/s10619-024-07444-6\",\"RegionNum\":4,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Distributed and Parallel Databases","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1007/s10619-024-07444-6","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
zk-Oracle: trusted off-chain compute and storage for decentralized applications
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.
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.
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