Homomorphic Encryptions for Privacy Preserving Vision

2026-06-23

AI Summary

This work addresses the challenge of deploying machine learning in the cloud when legal constraints prohibit sharing sensitive data by proposing a privacy-preserving image classification method based on fully homomorphic encryption (FHE). The authors redesign convolutional neural network architectures to operate efficiently in the encrypted domain, extending the TenSEAL framework to support multi-channel color images, multi-layer convolutions, and average pooling operations, with an efficient inference implementation built upon Microsoft SEAL. Experimental results demonstrate that the proposed approach achieves classification accuracy on MNIST, Kuzushiji-MNIST, Fashion-MNIST, and CIFAR-10 datasets nearly matching that of plaintext models, while maintaining relatively low computational overhead, thereby significantly advancing the practical applicability of FHE to real-world computer vision tasks.

Abstract

Legal requirements might prevent organizations from sharing sensitive data like medical or financial details of consumers which prevents them from leveraging cloud based ML-as-a-service solutions provided by third party providers, which are quickly gaining popularity these days. In this project, we aim to perform inference tasks in Computer Vision in a privacy-preserving manner, i.e, by only looking at encrypted data. Recent advances in fully homomorphic encryption make this possible. A fully homomorphic encryption allows an arbitrary sequence of additive and multiplicative operations to be performed on encrypted data directly. Applying homomorphic encryptions to CNNs requires modifying the conventional CNN layers, so that they adhere to the encryption scheme. Our aim was to explore the best methods to create CNNs which can classify encrypted images directly. We used Microsoft SEAL for performing homomorphic encryption. The performance of these "encryption based CNNs" should be comparable with baseline accuracies of the same CNNs trained on unencrypted data, and the aim was to achieve as low of a hit on inference-time performance as possible. We successfully obtained minimal drop in classification accuracy for various datasets. We used MNIST as our baseline, which is popularly used in related research work and then explored more complex datasets like Kuzushiji MNIST, Fashion-MNIST and CIFAR-10 as a part of our contribution. Additionally, we also added support for more complex operations on top of TenSEAL, like processing colored images (multi-channel input), applying multiple convolutional layers and performing average pooling.

Problem

• Homomorphic Encryption
• Privacy-Preserving
• Computer Vision
• Encrypted Inference
• CNN

Innovation

• Homomorphic Encryption
• Privacy-Preserving Inference
• Encrypted CNN
• FHE-based Vision
• Secure Machine Learning