BLOCKBENCH: A Framework for Analyzing Private Blockchains
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Abstract (arXiv)
Blockchain technologies are taking the world by storm. Public blockchains, such as Bitcoin and Ethereum, enable secure peer-to-peer applications like crypto-currency or smart contracts. Their security and performance are well studied. This paper concerns recent private blockchain systems designed with stronger security (trust) assumption and performance requirement. These systems target and aim to disrupt applications which have so far been implemented on top of database systems, for example banking, finance applications. Multiple platforms for private blockchains are being actively developed and fine tuned. However, there is a clear lack of a systematic framework with which different systems can be analyzed and compared against each other. Such a framework can be used to assess blockchains' viability as another distributed data processing platform, while helping developers to identify bottlenecks and accordingly improve their platforms. In this paper, we first describe BlockBench, the first evaluation framework for analyzing private blockchains. It serves as a fair means of comparison for different platforms and enables deeper understanding of different system design choices. Any private blockchain can be integrated to BlockBench via simple APIs and benchmarked against workloads that are based on real and synthetic smart contracts. BlockBench measures overall and component-wise performance in terms of throughput, latency, scalability and fault-tolerance. Next, we use BlockBench to conduct comprehensive evaluation of three major private blockchains: Ethereum, Parity and Hyperledger Fabric. The results demonstrate that these systems are still far from displacing current database systems in traditional data processing workloads. Furthermore, there are gaps in performance among the three systems which are attributed to the design choices at different layers of the software stack.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 12 Mar 2017.
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Citing papers per year: 2021: 1; 2022: 0; 2023: 2; 2024: 0; 2025: 1; 2026: 1.
5 papers.
- 2021Combining Graph Neural Networks with Expert Knowledge for Smart Contract Vulnerability Detectioncited by 34
- 2023A Survey on Secure and Private Federated Learning Using Blockchain: Theory and Application in Resource-constrained Computingcited by 3
- 2023A Model-Based Machine Learning Approach for Assessing the Performance of Blockchain Applicationscited by 0
- 2025HLF-FSL. A Decentralized Federated Split Learning Solution for IoT on Hyperledger Fabriccited by 0
- 2026Spec2Twin-Chain: Orchestrating Bi-Level Optimization with LLMs for Blockchain Digital Twin Constructioncited by 0
- 2026Spec2Twin-Chain: Orchestrating Bi-Level Optimization with LLMs for Blockchain Digital Twin Construction30 Aug 2026
- 2025HLF-FSL. A Decentralized Federated Split Learning Solution for IoT on Hyperledger Fabric10 Jul 2025
- 2023A Model-Based Machine Learning Approach for Assessing the Performance of Blockchain Applications20 Sep 2023
- 2023A Survey on Secure and Private Federated Learning Using Blockchain: Theory and Application in Resource-constrained Computing24 Mar 2023
- 2021Combining Graph Neural Networks with Expert Knowledge for Smart Contract Vulnerability Detection24 Jul 2021