Anthony Burke is an architect/engineer and CTO of UniqueMinds.ai. In this blog, he discusses the powerful potential of leveraging holomorphic encryption to safeguard protected health information.
The Power of Large Language Models: Large Language Models (LLMs) have revolutionized the way we process and interpret text. These advanced models, powered by artificial intelligence, are capable of understanding and generating human-like text and making complex judgment calls based on the input they receive. They can analyze free-form text, extract relevant information, and provide insights that were previously the domain of human experts. From diagnosing medical conditions to recommending personalized treatment plans, LLMs are transforming the healthcare landscape.
Ensuring Data Privacy with Homomorphic Encryption: As powerful as LLMs are, their use in healthcare raises significant privacy concerns. Patients are understandably wary of sharing sensitive information, such as their medical history or current health conditions. This is where homomorphic encryption comes into play. Homomorphic encryption allows data to be processed while still encrypted, meaning that the AI can analyze the information without ever seeing the actual data. This ensures that individuals retain full ownership and control over their personal information.
A Secure System for Clinical Trial Eligibility: Imagine a system that combines the analytical prowess of LLMs with the privacy guarantees of homomorphic encryption. Such a system could revolutionize the way patients are selected for clinical trials, especially for sensitive conditions like HIV+. Here’s how it would work:
- User Authentication: To prevent misuse, individuals would first authenticate themselves through a secure process. This ensures that only genuine users can access the system, protecting it from being flooded with fraudulent requests.
- Encrypted Data Submission: After authentication, users submit their medical data in an encrypted form. The homomorphic encryption ensures that this data remains unreadable to anyone but the user.
- LLM Analysis: The encrypted data is processed by the LLM. Thanks to the properties of homomorphic encryption, the LLM can perform its analysis without ever decrypting the data. It checks for eligibility based on the criteria of the clinical trial.
- Preapproval Feedback: The system provides a preapproval status based on the analysis. Users can see whether they are likely to be eligible for the trial without having to reveal any personal information. This empowers individuals to make informed decisions about their participation.
- Final Verification: If a user decides to proceed based on the preapproval, they can choose to reveal their decrypted information for final verification. This step ensures that the final admission to the trial is done with full transparency and human oversight.
Empowering Patients and Protecting Privacy: This innovative approach places control firmly in the hands of the patients. They can choose what information to reveal and when, significantly reducing the risk of exposure of sensitive data. By using homomorphic encryption, patients can engage with advanced AI systems without sacrificing their privacy.
Furthermore, this approach prevents potential gaming of the system. Since users must authenticate themselves and only authenticated data is processed, it becomes much harder for malicious actors to flood the system with bogus requests in an attempt to uncover trial conditions.
Conclusion: The integration of LLMs and homomorphic encryption offers a promising future for medical research and patient care. By ensuring that sensitive information remains private while still leveraging the power of AI, we can create a more secure and patient-centric healthcare environment. This system not only respects and protects patient privacy but also empowers individuals to make more informed decisions about their health and participation in medical research.
With such advancements, we are paving the way for a future where medical innovation and data privacy go hand in hand, providing the best outcomes for both patients and researchers.
