Privacy Risk
SMEs increasingly rely on data-driven decisions, but raw analytics can expose sensitive information and increase privacy risk.
Why this project
GDPR Pressure
Projects that process potentially sensitive data need stronger privacy-by-design thinking and more accountable data handling.
Practical Goal
DiffPriv-Gateway explores how Differential Privacy can be applied in a practical, modular, and explainable project setting.
How it works
Load Data
Input dataset and select the target metric or column.
Configure Privacy
Choose privacy parameters and sensitivity-aware settings.
Apply Mechanism
Use Laplace or Gaussian noise for privacy-preserving output.
Protected Result
Return post-processed, safer analytics-ready values.
Core capabilities
- Laplace mechanism support
- Gaussian mechanism support
- Clipping and rounding safeguards
- Modular privacy engine structure
- Testing and validation workflow
- GitHub-based issue and discussion tracking
- Wiki-supported documentation
- Academic / portfolio-ready project presentation
Project status
Current State
Phase 3 closeout and final repository organization.
Engineering
Requirements cleanup, passing tests, lint checks, and executable demo flow.
Documentation
README, Wiki, Discussions, and project board are all available publicly.