The problem EnDerCry AI solves

EnDerCry tackles a key issue in healthcare: safeguarding patient privacy amidst advancing diagnostic technologies. Traditional centralized data analysis poses privacy risks and erodes trust. EnDerCry employs Nillion's multiparty computation, allowing secure, distributed analysis without exposing raw patient data. This method supports collaborative medical image analysis, crucial for conditions like breast cancer, ensuring no single party accesses complete datasets. EnDerCry thus addresses the need for decentralized healthcare solutions, enhancing privacy while fostering collaboration and innovation. By leveraging multiparty computation, it offers a secure, efficient pathway for medical image analysis, balancing technological advancement with patient privacy. This represents a forward-thinking solution to healthcare's pressing challenges, paving the way for more secure, effective care.

Challenges we ran into

Developing EnDerCry presented several challenges, particularly in the realm of multiparty computation (MPC). Ensuring data privacy and security was paramount, yet implementing MPC required overcoming technical hurdles. The complexity of securely dividing computations across multiple parties without compromising data integrity was significant. Additionally, optimizing performance to ensure timely results without sacrificing security was another major challenge. Integrating this technology into existing healthcare workflows posed logistical difficulties, requiring careful coordination among various stakeholders. Lastly, educating both technical and non-technical users about the benefits and functionalities of EnDerCry was essential but challenging due to the specialized nature of the technology. Despite these obstacles, our team successfully navigated through them, delivering a robust solution that balances cutting-edge technology with user-friendly accessibility.