Skip to content

Latest commit

 

History

History
85 lines (66 loc) · 5.43 KB

README.md

File metadata and controls

85 lines (66 loc) · 5.43 KB
Raw

Quantifying the Blockchain Trilemma: A Comparative Study of Algorand and Ethereum 2.0

Overview

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.

Features

  • Comparative analysis of blockchain systems
  • Quantitative measurements of decentralization, efficiency, and security
  • Data-driven insights into blockchain technologies

Data

Data preparation

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

Data dictionary

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.

Proposer data (Algorand)

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.

Validator data (Beacon)

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.

Beacon transaction data

Column Name Discription
Timestamp The timestamp when the transactions were recorded
daily_transaction The daily transaction on Beacon (USD).

Algorand transaction data

Column Name Discription
time The timestamp when the transactions were recorded
count Transaction count per day.
fees Tokens used for transaction.

Methodologies to quantify on-chain decentralization

Shannon Entropy

A higher value indicates more chaos in authority distribution while a lower value refers to a more centralized system. We define the indice $H(v)$ as: $$H(v) = \prod \limits_{i=1}^N P(v_i)^{-P(v_i)}$$ where the $v_i$ refers to the unit data for each layer and the $P(v_i)$ refers to the weight of the unit data in respect to the overall dataset: $$P(v_i) = \frac{v_i}{\sum_{i=1}^N v_i}$$

Gini Coefficient

$$G = 1 - \sum_{i=1}^N P_i^2$$

Nakaoto Coefficient

$$ N = min{k \in [1,...,K] : \sum_{i=1}^K P_i > 0.51}$$

Herfindahl Hirschman Index

$$HHI = \sum_{i=1}^N P_i^2$$

blockchain.drawio

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.

Usage

To execute the Jupyter Notebook and perform the analysis:

  1. Navigate to the directory where you cloned the repository.
  2. Start the Jupyter Notebook server by running jupyter notebook in your command line.
  3. Open the experiment.ipynb file from the Jupyter Notebook interface in your browser.
  4. Execute the notebook cells in sequence to conduct the analysis.

Contributing

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.

License

This project is licensed under [MIT License]. For more details, see the LICENSE file in the repository.

Contact

For any inquiries or potential collaborations, please reach out to the project maintainers at [lz183@duke.edu]. We are open to feedback and interested in hearing about how you may want to use or improve the project.