This advancement allows AI systems to analyze encrypted data without decrypting it, ensuring privacy throughout the process.
FHE has long been considered a major breakthrough in cryptography because it enables computations on encrypted information while keeping it secure. However, applying this method to deep learning has been challenging due to the heavy computational requirements and technical constraints. Orion addresses these challenges by automating the conversion of deep learning models into FHE-compatible formats.
The researchers’ study, recently published on arXiv and set to be presented at the 2025 ACM International Conference on Architectural Support for Programming Languages and Operating Systems, highlights Orion’s ability to make privacy-focused AI more practical.
One of the biggest concerns in AI today is that machine learning models require direct access to user data, raising serious privacy risks. Orion eliminates this issue by allowing AI to function without exposing sensitive information. The framework is built to work with PyTorch, a widely used machine learning library, making it easier for developers to integrate FHE into existing models.
Orion also introduces optimization techniques that reduce computational burdens, making privacy-preserving AI more efficient and scalable.
Orion has demonstrated notable performance improvements, achieving speeds 2.38 times faster than previous FHE deep learning methods. The researchers successfully implemented high-resolution object detection using the YOLO-v1 model, which contains 139 million parameters—a scale previously considered impractical for FHE. This progress suggests Orion could enable encrypted AI applications in sectors like healthcare, finance, and cybersecurity, where protecting user data is essential.
A key advantage of Orion is its accessibility. Traditional FHE implementations require specialized knowledge, making them difficult to adopt. Orion simplifies the process, allowing more developers to use the technology without extensive training. By open-sourcing the framework, the research team hopes to encourage further innovation and adoption. As AI continues to expand into everyday life, advancements like Orion could help ensure that technological progress does not come at the cost of privacy and security.
