Skip to content

Latest commit

Β 

History

History
75 lines (49 loc) Β· 2.68 KB

File metadata and controls

75 lines (49 loc) Β· 2.68 KB

πŸš€ Privagen: Privacy-Preserving Synthetic Data Platform

Privagen is a powerful synthetic data generation platform built for modern enterprises, AI teams, and analysts who want to unlock sensitive data β€” without risking privacy or compliance.

This tool lets you upload real-world datasets and instantly receive statistically accurate, privacy-preserving synthetic versions. Whether you're working in healthcare, finance, or education, Privagen makes data shareable, safe, and usable.


🧠 Why Privagen?

Organizations today are paralyzed by privacy concerns and data access bottlenecks. Analysts, researchers, and engineers often wait weeks β€” or are denied access altogether β€” to datasets that could unlock insights or innovation.

Privagen solves this by generating safe synthetic data that preserves the structure, correlations, and statistical patterns of real data β€” without exposing any sensitive information.


✨ Features

πŸ” Smart Model Recommendation

Automatically detects the best synthetic generation algorithm (CTGAN, TVAE, GaussianCopula) based on your data’s structure β€” no ML experience needed.

πŸ“Š Insight Dashboard

Compare real and synthetic data through:

  • Correlation heatmaps
  • Summary statistics
  • Mean difference analysis

πŸ›‘ Privacy Risk Scoring

Advanced nearest-neighbor distance analysis ensures your synthetic data isn’t β€œtoo real.” Know exactly how safe your output is.

βœ… Differential Privacy Mode

Toggle on a privacy-first training mode that limits training intensity and reduces memorization risk, perfect for regulated industries.

πŸ“ Downloadable Reports

Export:

  • Synthetic CSVs
  • Insight comparison reports
  • Privacy risk audit summaries

πŸ” Use Cases

Sector Use Case
πŸ₯ Healthcare Share EHRs and patient data without violating HIPAA
πŸ’° Finance Build fraud detection models without exposing accounts
πŸ§‘β€πŸŽ“ Education Train AI on student performance data without FERPA risk
πŸ§ͺ Research Safely open datasets to collaborators or open source

πŸš€ Getting Started

  1. Clone this repository
  2. Install dependencies:
pip install -r requirements.txt
  1. Run the app:
python app.py
  1. Upload any .csv dataset and generate a safe synthetic version with dashboards and risk reports


πŸ“¬ Questions or Feedback?

We’re building Privagen to empower ethical data science and secure collaboration. If you have feedback, feature requests, or want to collaborate β€” let’s connect.