The Data Transparency Compliance project focuses on building a robust algorithm to assess the degree of compliance of e-commerce platforms with data transparency regulations, including GDPR, CCPA, and other relevant norms applicable in India. The primary objective is to identify potential violations and provide a clear indication of the level of compliance.
The project encompasses the following key aspects:
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Algorithm Development π οΈ:
- Design and implement an algorithm that evaluates e-commerce platforms for compliance with data transparency regulations.
- Consider multiple regulatory frameworks, such as GDPR, CCPA, and any specific norms applicable in the Indian context.
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Degree of Compliance Assessment π:
- Establish metrics and criteria to measure the degree of compliance.
- Develop a scoring system or classification to categorize the level of adherence to data transparency regulations.
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Violation Detection
β οΈ :- Implement mechanisms to identify potential violations of data transparency norms.
- Provide detailed insights into specific aspects of non-compliance.
Follow the steps below to set up and use the Data Transparency Compliance Assessment tool:
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Clone the repository to your local machine:
git clone https://github.com/Thennavan-Hex/Dark-Patterns.git
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Navigate to the Extension directory:
cd Dark-Patterns
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Navigate to the Server directory:
cd Server -
Install Node.js dependencies:
npm install
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Start the server:
node server.js
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Ensure you have the required dependencies installed:
pip install -r requirements.txt
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Run the compliance assessment algorithm:
python assess_compliance.py
View the results of the compliance assessment in the generated reports or logs. Take appropriate actions based on identified violations and improve the platform's data transparency practices.
This project is designed to assist e-commerce platforms in enhancing their data transparency practices. It is important to regularly update the algorithm to align with evolving regulatory requirements.
- Thennavan
- Prathina
- Niranjana V K
- Abhishek J L
- Sudharsan R