LYRA

Privacy-First AI SDK for Fitness Studios

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Inspiration

As someone who loves fitness & who's experienced data breaches firsthand, I noticed a serious problem: fitness studios are caught in an difficult situation. They need to analyze member data to improve services, but 80% of fitness apps share user data with third parties - often without proper consent.

When studios mishandle sensitive health data, they face massive fines and lose member trust. I've seen this happen at spaces I didn't want them to, where a data leak caused several others (including me) to stop using the service.

Even while having only some background in cybersecurity, I wanted to create something that would protect member privacy while still giving studios the insights they need to help people stay healthy and avoid injuries.

What it does

LYRA AI SDK is a privacy-first fitness intelligence toolkit that helps developers build apps that can identify injury risks without compromising member data. It uses:

  • Differential privacy to protect individuals
  • Federated Learning that keeps raw data inside the studio's systems
  • Anonymization that replaces personal identifiers with secure UUIDs

Developers can integrate LYRA into fitness applications, and the SDK provides AI-powered insights about injury risk while maintaining strong privacy guarantees.

How I built it

  • Python backend with a custom Flask API
  • Streamlit for the frontend dashboard demo
  • Docker for containerization and easy deployment
  • Custom differential privacy algorithms with AI prediction models
  • Created synthetic data for testing without risking real PII

The architecture separates the frontend and backend services to maintain better security boundaries. All sensitive processing happens in isolated containers, and I implemented a privacy budget tracker that shows exactly how much privacy is being "spent" on each analysis.

Challenges I ran into

Building LYRA alone was definitely challenging:

  • the time constraint made creating a polished UI difficult
  • implementing differential privacy correctly required researchhhh
  • debugging
  • struggled so much with Docker deploying took longer than expected
  • finding time to work on every aspect of the project by myself was very hard

Accomplishments that I'm proud of

  • I created a functional privacy-first AI SDK from scratch
  • successfully implemented differential privacy algorithms that actually work
  • built something that developers can easily integrate into fitness applications
  • added a motivational quotes feature that makes the app more engaging & fun
  • finally managed to package everything in Docker for easy deployment

What I learned

This project taught me so much:

  • federated learning is powerful but can pretty complex to implement correctly
  • docker makes deployment easier but makes things complex during development
  • streamlit is not just pretty rad for UI it's lit
  • working alone on a complex project requires ruthless prioritization & time (hehe)

What's next for LYRA

I'm honestly just getting started with LYRA

  • I would like to update the UI & UX for a more polished experience
  • Adding personalized training recommendations based on AI risk analysis
  • Implementing a proper federated learning system across multiple studios
  • Creating an open API for fitness wearable integration
  • Expanding the ML models with deep learning + fine tuning

This MVP is just the beginning. I believe LYRA can change how developers handle sensitive data while improving health outcomes, especially in a world where data breaches are becoming more common.

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