A privacy preserving machine learning framework for medical image analysis using quantized fully connected neural networks with TFHE based inference: S …

Analysis

Why This Paper Matters

Medical image analysis is a critical application of AI, but it involves sensitive patient data that must be protected. Traditional machine learning approaches require access to raw data, raising privacy concerns and regulatory challenges. This paper addresses the need for privacy-preserving machine learning (PPML) in healthcare by proposing a framework that combines fully connected neural networks (FCNNs) with TFHE-based homomorphic encryption. By enabling inference on encrypted data, the framework allows medical image analysis without exposing patient information, which is essential for compliance with regulations like HIPAA and GDPR.

The use of TFHE is particularly noteworthy because it supports fast bootstrapping and efficient evaluation of arbitrary functions, making it suitable for secure neural network inference. However, TFHE-based inference is computationally demanding, and the choice of FCNNs over convolutional networks may reflect a trade-off between privacy and performance. This paper contributes to the growing body of work on PPML and highlights the potential of homomorphic encryption for secure medical AI.

Technical Contributions

• Privacy-Preserving Framework: Integrates TFHE homomorphic encryption with a quantized FCNN to enable secure inference on encrypted medical images.
• Quantized Neural Network: Employs quantization to reduce the computational overhead of homomorphic operations, making the approach more practical.
• Evaluation on MedMNIST: Uses the MedMNIST dataset, a standardized collection of medical images, to benchmark the framework's performance.
• Secure Inference Protocol: Details the process of encrypting inputs, performing homomorphic computations, and decrypting outputs, ensuring end-to-end privacy.

Results

The abstract does not provide specific quantitative results. However, the paper likely reports classification accuracy on MedMNIST tasks and measures computational latency and ciphertext size. Comparisons with non-private baselines and other PPML methods may be included. Given the use of FCNNs, the accuracy might be lower than convolutional networks, but the focus is on privacy guarantees. The results section would typically demonstrate the feasibility of TFHE-based inference for medical images, albeit with potential overhead.

Significance

This work advances the field of privacy-preserving medical AI by demonstrating a practical implementation of homomorphic encryption for image analysis. It underscores the importance of balancing privacy with utility and provides a foundation for future research on more efficient encrypted inference techniques. As healthcare increasingly adopts AI, frameworks like this can facilitate secure data sharing and collaborative analysis across institutions without compromising patient confidentiality. The use of TFHE and quantized networks may inspire further optimizations and adaptations to more complex models like CNNs.