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🚀 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 — 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 — or are denied access altogether — 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 — without exposing any sensitive information.


✨ Features

🔁 Smart Model Recommendation

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

📊 Insight Dashboard

Compare real and synthetic data through:

  • Correlation heatmaps
  • Summary statistics
  • Mean difference analysis

🛡 Privacy Risk Scoring

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

✅ Differential Privacy Mode

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

📁 Downloadable Reports

Export:

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

🔍 Use Cases

Sector Use Case
🏥 Healthcare Share EHRs and patient data without violating HIPAA
💰 Finance Build fraud detection models without exposing accounts
🧑‍🎓 Education Train AI on student performance data without FERPA risk
🧪 Research Safely open datasets to collaborators or open source

🚀 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


📬 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 — let’s connect.

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