Confidential and Protected Disease Classifier using Fully Homomorphic Encryption

Abstract: With the rapid surge in the prevalence of LLMs, individuals are increasingly turning to conversational AI for initial insights across various domains, including health-related inquiries such as disease diagnosis. Many users seek potential causes on platforms like ChatGPT or Bard before consulting a medical professional for their ailment. These platforms offer valuable benefits by streamlining the diagnosis process, alleviating the significant workload of healthcare practitioners, and saving users both time and money by avoiding unnecessary doctor visits. However, Despite the convenience of such platforms, sharing personal medical data online poses risks, including the presence of malicious platforms or potential eavesdropping by attackers. To address privacy concerns, we propose a novel framework combining FHE and Deep Learning for a secure and private diagnosis system. Operating on a question-and-answer-based model akin to an interaction with a medical practitioner, this end-to-end secure system employs Fully Homomorphic Encryption (FHE) to handle encrypted input data. Given FHE's computational constraints, we adapt deep neural networks and activation functions to the encryted domain. Further, we also propose a faster algorithm to compute summation of ciphertext elements. Through rigorous experiments, we demonstrate the efficacy of our approach. The proposed framework achieves strict security and privacy with minimal loss in performance.

• The paper presents a system that leverages FHE to perform disease classification without ever decrypting sensitive patient data.
• The methodology adapts neural network operations, including modifications to fully connected layers and activation functions via polynomial approximations.
• Experimental results demonstrate high diagnostic accuracy, paving the way for scalable, privacy-preserving healthcare diagnostics.

Understanding Secure Disease Classification Through Fully Homomorphic Encryption

Introduction to Homomorphic Encryption and Healthcare Diagnosis

The fusion of advanced cryptography and healthcare diagnostics has led to developments like Fully Homomorphic Encryption (FHE), which allows for computations on encrypted data without ever needing to decrypt it. This capability is crucial in environments where data privacy is paramount, such as in medical diagnostics. FHE enables the encryption key to remain exclusively with the user, ensuring that sensitive medical data remains confidential throughout the diagnostic process.

Fully Homomorphic Encryption: Basics

FHE is an encryption method allowing data to remain encrypted during both storage and processing phases, ensuring the confidentiality of computations. Different types of operations such as addition and multiplication can be directly applied to encrypted data. The Fully Homomorphic Encryption comes with computational overhead, but offers limitless possibilities in maintaining data privacy.

Related Works: Usage of FHE in Healthcare

Various projects and research efforts have successfully utilized FHE for secure disease prediction. These applications range from heart disease diagnosis to systems designed for encrypted cancer prediction. This ensures compliance with stringent healthcare regulations while also maintaining the privacy and security of sensitive health data.

Applying FHE to Disease Classification

The discussed paper presents a system where Fully Homomorphic Encryption is employed for secure disease prediction. The process involves using encrypted patient data to deliver diagnostic results that only the patient can decrypt, thereby upholding data confidentiality. The implementation adjusts standard deep learning functions, such as activation functions, to work within the constraints imposed by FHE.

Fully Connected Layer Adaptation

In typical neural networks, layers and functions (such as the fully connected layers and activation functions) process plaintext data effortlessly. FHE, however, introduces challenges where traditional operations need to be adapted or approximated for encrypted computations. For instance, the summation of encrypted outputs required a unique approach involving a rotate and add algorithm, optimizing the handling of encrypted data.

Activation Functions: ReLU and Its Approximations

When dealing with FHE, non-linear activation functions like ReLU need modifications to operate in the encrypted domain. This was tackled by approximating these functions using polynomial regression, allowing for non-linear operations to be computed while the data remains encrypted. However, these approximations can introduce new challenges, such as decreased model accuracy.

Performance and Implications

The experiments demonstrate that the adaptions for FHE could perform fairly well, with the disease classifier maintaining high accuracy. This showcases the feasibility of using FHE in practical applications without sacrificing significant performance. Furthermore, these adaptations could pave the way for more secure medical diagnostic systems, potentially influencing how sensitive medical data is handled globally.

Future Directions

Looking forward, the implementation of FHE in disease diagnosis has room for enhancement. Improvements could include more efficient computational strategies or broader application scopes, such as integrating this system into larger scale health informatics platforms. Moreover, further research could explore the integration of FHE with other types of neural network architectures or expanding the system to support a more interactive, dialogue-based user experience.

Conclusion

The integration of Fully Homomorphic Encryption into disease classification systems presents a promising frontier in the intersection of cryptography and healthcare. This approach not only adheres to the strict privacy requirements demanded by healthcare regulations but also opens the door to future advancements that could transform secure medical diagnostics. As this field evolves, the potential for FHE to secure an ever-widening array of applications will undoubtedly grow, reinforcing the significance of privacy-preserving technologies in today's digital and data-driven world.