Helix AI | Devpost

Inspiration

In today’s digital age, individuals generate vast amounts of valuable health data daily through devices like smartwatches and fitness trackers. This data, encompassing metrics such as blood pressure, oxygen levels, and activity patterns, holds immense potential for advancing scientific research, driving healthcare innovations, and ultimately saving lives. However, stringent regulations like HIPAA, PDPA, and GDPR create significant barriers to cross-border data sharing, leaving this data underutilized. Healthcare institutions face administrative burdens and legal risks when attempting to exchange or monetize health information, resulting in fragmented systems that hinder research and optimal patient care. Recognizing the lost economic and medical opportunities, we envisioned a solution that empowers individuals to monetize their health data securely while facilitating seamless data sharing for research and innovation.

What it does

Helix AI is a decentralized application that leverages the efficiency of the XRPL (XRP Ledger) and the programmability of EVM (Ethereum Virtual Machine) sidechains to create a secure marketplace for healthcare data. Helix AI serves both patients and institutions by ensuring privacy, transparency, and cost-effective data sharing. Users can effortlessly monetize their health data by granting consent through our app, initially focusing on data extracted from devices like the Apple Watch. This data is then utilized to create machine learning models that expedite research in the healthcare industry. By maintaining robust privacy protections through blockchain technology and advanced machine learning techniques like federated learning and differential privacy, Helix AI ensures that sensitive health data remains secure and anonymized. Pharmaceutical companies and researchers gain access to larger, more robust datasets, accelerating their research and innovation while users retain control and can earn revenue from their data.

How we built it

Helix AI was developed by integrating cutting-edge technologies to create a secure and privacy-centric ecosystem for health data sharing: 1. Blockchain Integration: • Utilized XRPL and EVM sidechains to deploy smart contracts that manage data transactions and token incentives. • Implemented decentralized storage using IPFS to securely reference user data without storing sensitive information on-chain. • Leveraged Ripple’s stablecoins for seamless token rewards and conversions to cash. 2. Machine Learning: • Incorporated federated learning to train machine learning models across distributed devices, ensuring raw data never leaves the user’s device. • Applied differential privacy techniques to add controlled noise to model parameters, safeguarding individual data contributions. 3. User Interface & Experience: • Developed a user-friendly app allowing users to register, manage their data, and track their earnings. • Created a marketplace UI where data providers can list their datasets and buyers can purchase access using tokens. 4. Security Protocols: • Implemented a two-layer security system combining blockchain’s decentralized infrastructure with advanced encryption and privacy-preserving machine learning pipelines. 5. Development Roadmap: • Followed a phased approach from prototyping and core feature development to federated learning integration, marketplace expansion, and scaling through strategic partnerships.

Challenges we ran into

1.  Data Privacy and Security:

Implementing robust privacy-preserving techniques such as federated learning and differential privacy while maintaining model accuracy was technically challenging. 2. Interoperability: Ensuring seamless integration with various data sources and wearable devices, especially given the fragmented nature of EHR systems, required extensive standardization and validation. 3. Scalability: Designing a platform capable of handling high-volume transactions and large-scale federated learning tasks without compromising performance demanded advanced architectural solutions.

Accomplishments that we’re proud of 1. Functional Prototype: Successfully developed a working prototype demonstrating secure data sharing and smart contract functionality on XRPL and EVM sidechains. 2. Advanced Privacy Integration: Implemented a two-layer security protocol combining blockchain security with federated learning and differential privacy, ensuring robust data protection. 4. Scalable Architecture: Designed a scalable microservices architecture capable of handling increased data volumes and transaction loads, preparing Helix AI for future growth.

What we learned 1. Importance of Privacy by Design: Integrating privacy-preserving technologies from the ground up is crucial for building trust and ensuring compliance in health data applications. 2. Balancing Security and Usability: Achieving a seamless user experience while maintaining high security and privacy standards requires careful design and iterative testing. 3. Navigating Regulatory Landscapes: Understanding and adhering to international data protection regulations is essential for cross-border data sharing and requires ongoing attention as laws evolve. 4. Collaborative Development: Building a platform that serves diverse stakeholders—users, healthcare providers, researchers—necessitates effective collaboration and communication across multidisciplinary teams. 5. Scalability Challenges: Designing systems that can scale efficiently while maintaining performance and security is a complex but achievable goal with the right architectural strategies.

What’s next for Helix AI 1. Expand Data Sources: Integrate additional wearable devices and EHR systems to broaden the range of health data available on the platform, enhancing dataset diversity and robustness. 2. Enhance AI Capabilities: Develop more sophisticated machine learning models and analytics tools to provide deeper insights and more valuable data for researchers and pharmaceutical companies. 3. Global Expansion: Establish partnerships with international healthcare organizations and research institutions to facilitate cross-border data sharing and collaboration. 4. Advanced Privacy Features: Continuously improve privacy-preserving techniques and explore new technologies to stay ahead of potential security threats and maintain user trust. 5. User Empowerment Tools: Introduce advanced data management tools that allow users to customize their data sharing preferences, track earnings in real-time, and gain insights into how their data is being used. 6. Regulatory Compliance Automation: Develop automated compliance tools to simplify adherence to evolving regulations, reducing administrative burdens for healthcare institutions and ensuring ongoing legal compliance. 7. Community and Ecosystem Building: Foster a vibrant community of users, developers, and partners through initiatives like hackathons, developer grants, and collaborative projects to drive innovation and platform growth.

By continuing to innovate and expand, Helix AI aims to revolutionize healthcare data sharing, unlocking its full potential for scientific breakthroughs, improved patient care, and economic growth worldwide.