@inproceedings{b12f9e0d9ec94e909852a26257fa8999,
title = "VChain: Enabling verifiable boolean range queries over blockchain databases",
abstract = "Blockchains have recently been under the spotlight due to the boom of cryptocurrencies and decentralized applications. There is an increasing demand for querying the data stored in a blockchain database. To ensure query integrity, the user can maintain the entire blockchain database and query the data locally. However, this approach is not economic, if not infeasible, because of the blockchain's huge data size and considerable maintenance costs. In this paper, we take the first step toward investigating the problem of verifiable query processing over blockchain databases. We propose a novel framework, called vChain, that alleviates the storage and computing costs of the user and employs verifiable queries to guarantee the results' integrity. To support verifiable Boolean range queries, we propose an accumulator-based authenticated data structure that enables dynamic aggregation over arbitrary query attributes. Two new indexes are further developed to aggregate intra-block and inter-block data records for efficient query verification. We also propose an inverted prefix tree structure to accelerate the processing of a large number of subscription queries simultaneously. Security analysis and empirical study validate the robustness and practicality of the proposed techniques.",
keywords = "Blockchain, Data integrity, Query processing",
author = "Cheng Xu and Ce Zhang and Jianliang Xu",
note = "Funding Information: This work was supported by Research Grants Council of Hong Kong under GRF Projects 12201018, 12200817, 12244916, CRF Project C1008-16G, NSFC Grant 61502258, and Major Technology Innovation Project of Shan-dong 2018CXGC0703. Publisher copyright: {\textcopyright}2019 Association for Computing Machinery; 2019 International Conference on Management of Data, SIGMOD 2019 ; Conference date: 30-06-2019 Through 05-07-2019",
year = "2019",
month = jun,
day = "25",
doi = "10.1145/3299869.3300083",
language = "English",
series = "Proceedings of the ACM SIGMOD International Conference on Management of Data",
publisher = "Association for Computing Machinery (ACM)",
pages = "141--158",
booktitle = "Proceedings of the 2019 International Conference on Management of Data, SIGMOD 2019",
address = "United States",
}