BSc IT Project Guide: Anonymization Tool
1. Project Title
Anonymization Tool to Anonymize Sensitive Data for Privacy
2. Objective
The objective of this project is to develop a tool that anonymizes sensitive data to ensure privacy and compliance with data protection regulations such as GDPR and HIPAA. The tool will support methods like masking, tokenization, generalization, and pseudonymization to anonymize datasets effectively.
3. Scope
- Supports various anonymization techniques
- Processes structured data (CSV, Excel, etc.)
- Provides configuration options for different anonymization strategies
- Generates logs and reports of anonymization activities
- Ensures minimal data utility loss while anonymizing
4. Software Requirements
- Python 3.x
- Flask or Streamlit (for UI)
- Pandas, NumPy
- Scikit-learn (optional, for testing data utility)
- SQLite or file-based logging
- HTML/CSS/JS (if building a web interface)
5. Functional Requirements
- Load dataset and identify sensitive fields
- Choose anonymization techniques per field
- Preview changes before applying
- Export anonymized dataset
- Track changes and provide audit logs
6. Non-Functional Requirements
- Usability: User-friendly interface
- Performance: Efficient processing for large datasets
- Security: No sensitive data retained after processing
- Scalability: Support for future enhancements like API integration
7. Methodology
The project will follow an Agile methodology with iterative
development and regular feedback. The primary steps include:
- Requirement gathering and design
- Implementation of anonymization logic
- Development of user interface
- Testing and validation of results
- Documentation and deployment
8. Expected Outcome
A fully functional tool that can anonymize sensitive data in various formats while preserving the utility of non-sensitive information. The system will be suitable for use in academic, business, and healthcare settings.
9. Future Enhancements
- Real-time anonymization API
- Integration with cloud storage
- Enhanced privacy risk assessment metrics
- Machine learning-based adaptive anonymization
10. References
- https://en.wikipedia.org/wiki/Data_anonymization
- https://www.iso.org/standard/69373.html
- https://gdpr.eu/data-anonymization/
- Academic papers on data privacy and anonymization techniques