This project aims to provide a comprehensive analysis of blockchain technologies, focusing on their efficiency, security, and decentralization aspects. It includes comparative studies of systems like Algorand and Beaconchain, offering insights into the blockchain trilemma.
- Comparative analysis of blockchain systems
- Quantitative measurements of decentralization, efficiency, and security
- Data-driven insights into blockchain technologies
Acquire Algorand data from Bitquery, and acquire Beacon data from Beacon Explorer by using SIPDER framework.
The data was stored in the Algorand_data and Beacon_chain_data
We evaluate the decentralization in two layers, the consensus layer and the transaction layer.
For the consensus layer, we use proposer/validator data to represent the staking or voting process.
Column Name | Discription |
---|---|
date |
The specific date for which the data is recorded, formatted as "YYYY-MM-DD". |
proposer_count |
The number of proposers that participated in the block proposal process on the given date. |
reward |
The Reward for block proposal per day. |
Column Name | Discription |
---|---|
date |
The specific date for which the data is recorded, formatted as "YYYY-MM-DD". |
Value |
Average account balance of validators per day. |
For the transaction layer, we use the number of daily transaction data to quantify the decentralization.
Column Name | Discription |
---|---|
Timestamp |
The timestamp when the transactions were recorded |
daily_transaction |
The daily transaction on Beacon (USD). |
Column Name | Discription |
---|---|
time |
The timestamp when the transactions were recorded |
count |
Transaction count per day. |
fees |
Tokens used for transaction. |
A higher value indicates more chaos in authority distribution while a lower value refers to a more centralized system. We define the indice
The figure 6 in the paper is this one. The companies shown below are pioneers in fusing blockchain technology with federated analysis. These initiatives show the potential of a wide range of uses for federated analytics and blockchain technology, including decentralized data analysis, distributed machine learning, and data privacy protection. The potential for this convergence exists to augment the function of blockchain technology in the digital economy. In summary, these initiatives offer practical applications for blockchain technology.
To execute the Jupyter Notebook and perform the analysis:
- Navigate to the directory where you cloned the repository.
- Start the Jupyter Notebook server by running
jupyter notebook
in your command line. - Open the
experiment.ipynb
file from the Jupyter Notebook interface in your browser. - Execute the notebook cells in sequence to conduct the analysis.
We welcome contributions from the community. If you wish to contribute to the project, please follow these guidelines:
- Fork the repository and create a feature branch.
- Make your changes and ensure that your code adheres to the existing style.
- Submit a pull request with a clear description of your improvements or bug fixes.
This project is licensed under [MIT License]. For more details, see the LICENSE file in the repository.
For any inquiries or potential collaborations, please reach out to the project maintainers at [[email protected]]. We are open to feedback and interested in hearing about how you may want to use or improve the project.