A systematic literature review of blockchain and smart contract development: Techniques, tools, and open challenges

Blockchain technology has attracted much attention due to the great success of the cryptocurrencies. Owing to its immutability property and consensus protocol, blockchain offers a new solution for trusted storage and computation services. To scale up the services, prior research has suggested a hybrid storage architecture, where only small meta-data are stored on-chain and the raw data are outsourced to off-chain storage. To protect data integrity, cryptographic proof can be constructed online for queries over the data stored in the system. However, the previous schemes only support simple key-value queries. In this paper, we take the first step toward studying authenticated range queries in the hybrid-storage blockchain. The key challenge lies in how to design an authenticated data structure (ADS) that can be efficiently maintained by the blockchain, in which a unique gas cost model is employed. By analyzing the performance of the existing techniques, we propose a novel ADS, called GEM^2-tree, which is not only gas-efficient but also effective in supporting authenticated queries. To further reduce the ADS maintenance cost without sacrificing much the query performance, we also propose an optimized structure, GEM^2*-tree, by designing a two-level index structure. Theoretical analysis and empirical evaluation validate the performance of the proposed ADSs.

GEM^2-Tree: A Gas-Efficient Structure for Authenticated Range Queries in Blockchain

The success of Bitcoin and other cryptocurrencies bring enormous interest to blockchains. A blockchain system implements a tamper-evident ledger for recording transactions that modify some global states. The system captures entire evolution history of the states. The management of that history, also known as data provenance or lineage, has been studied extensively in database systems. However, querying data history in existing blockchains can only be done by replaying all transactions. This approach is applicable to large-scale, offline analysis, but is not suitable for online transaction processing.We present LineageChain, a fine-grained, secure and efficient provenance system for blockchains. LineageChain exposes provenance information to smart contracts via simple and elegant interfaces, thereby enabling a new class of blockchain applications whose execution logics depend on provenance information at runtime. LineageChain captures provenance during contract execution, and efficiently stores it in a Merkle tree. LineageChain provides a novel skip list index designed for supporting efficient provenance query processing. We have implemented LineageChain on top of Hyperledger and a blockchain-optimized storage system called ForkBase. Our extensive evaluation of LineageChain demonstrates its benefits to the new class of blockchain applications, its efficient query, and its small storage overhead.

Fine-grained, secure and efficient data provenance on blockchain systems

Ethereum, the largest blockchain for running smart contracts, charges the people who send transactions to deploy or invoke smart contracts for thwarting resource abuse. The amount of transaction fee depends on the size of that contract and the operations executed by that contract. Consequently, smart contracts with inefficient code will waste money. In this article, we propose and develop the first tool, named GasChecker , for automatically identifying gas-inefficient code in smart contracts, and conduct the first empirical study on the prevalence of gas-inefficient code in the deployed smart contracts. More precisely, we first summarize ten gas-inefficient programming patterns and propose a new approach based on symbolic execution (SE) to detect them in the bytecode of smart contracts. To make our approach scalable to analyze millions of smart contracts, we parallelize SE by tailoring it to the MapReduce programming model, and propose a new feedback-based load balancing strategy to effectively utilize cloud resources. Extensive experiments show that GasChecker scales well with the increase of workers. The empirical study demonstrates that lots of real smart contracts contain various inefficient code. Manual investigation demonstrates that only 2.5 percent of discovered gas-inefficient instances are false positives.

GasChecker: Scalable Analysis for Discovering Gas-Inefficient Smart Contracts

Nowadays an emerging class of applications are based oncollaboration over a shared database among different entities. However, the existing solutions on shared database may require trust on others, have high hardware demand that is unaffordable for individual users, or have relatively low performance. In other words, there is a trilemma among security, compatibility and efficiency. In this paper, we present FalconDB, which enables different parties with limited hardware resources to efficiently and securely collaborate on a database. FalconDB adopts database servers with verification interfaces accessible to clients and stores the digests for query/update authentications on a blockchain. Using blockchain as a consensus platform and a distributed ledger, FalconDB is able to work without any trust on each other. Meanwhile, FalconDB requires only minimal storage cost on each client, and provides anywhere-available, real-time and concurrent access to the database. As a result, FalconDB over-comes the disadvantages of previous solutions, and enables individual users to participate in the collaboration with high efficiency, low storage cost and blockchain-level security guarantees.

https://dl.acm.org/doi/pdf/10.1145/3318464.3380594

FalconDB: Blockchain-based Collaborative Database

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.

vChain: Enabling Verifiable Boolean Range Queries over Blockchain Databases

/pdf/slimchain-scaling-blockchain-transactions-through-off-chain-3hc2e87b4y.pdf

SlimChain: scaling blockchain transactions through off-chain storage and parallel processing

Blockchain has emerged as a promising solution for secure data storage and retrieval for decentralized applications. To scale blockchain systems, a prevailing approach is to employ a hybrid storage model, where only small meta-data are stored on-chain while the raw data are outsourced to an off-chain storage service provider. The key issue for query processing in such a system is the design of gas-efficient authenticated data structure (ADS) to authenticate the query results. In this paper, we study novel ADS schemes for authenticated keyword search in hybrid-storage blockchains. We first propose the Suppressed Merkle inverted (Merkleinv) index, which maintains only a partial ADS structure on-chain that can be securely updated with a logarithm-sized cryptographic proof. Moreover, we propose a Chameleon inverted (Chameleoninv) index that leverages the chameleon vector commitment to achieve a constant maintenance cost. It is further optimized with Bloom filters to enhance the query and verification performance. We prove the security of the proposed ADS schemes and evaluate their performance using real datasets on the Ethereum platform. Experimental results show that, compared to a baseline solution, the proposed Merkleinv and Chameleoninv indexes reduce the average on-chain maintenance cost from US$10.39 down to US$2.50 and US$0.24, respectively, without sacrificing much the query performance.

Ce Zhang

Papers

vChain: Enabling Verifiable Boolean Range Queries over Blockchain Databases

vChain: Enabling Verifiable Boolean Range Queries over Blockchain Databases

GEM^2-Tree: A Gas-Efficient Structure for Authenticated Range Queries in Blockchain

SlimChain: scaling blockchain transactions through off-chain storage and parallel processing

Authenticated Keyword Search in Scalable Hybrid-Storage Blockchains