UTS researchers achieve breakthrough in privacy-preserving AI

The breakthrough: encrypted intelligence

The UTS research team created a world-first privacy-preserving framework for DRL using FHE. Unlike traditional encryption, which requires data to be decrypted before it can be processed, FHE allows AI models to learn and make decisions while the data remains fully protected.

The core technical innovation is the development of an HE-compatible Adam optimiser. Modern AI training depends heavily on non-linear mathematical operations, including functions such as inverse square roots, which are extremely difficult to compute on encrypted data and typically break under homomorphic encryption.  

The team successfully redesigned these learning algorithms to bypass the need for high-degree polynomial approximations of inverse square roots on encrypted data, and maximised learning efficiency for the DRL agent.

Early results are promising. The encrypted DRL model performs within 10% accuracy of standard, unencrypted techniques – all while maintaining full data confidentiality. 

In the proposed system, the user protects its privacy by encrypting all system information before sharing it with an external AI agent. The AI learns how to make decisions directly from this encrypted data, without ever seeing the original information. The decisions it produces also remain encrypted and are only decrypted and applied by the user locally. This process repeats until the system achieves the desired performance, ensuring strong privacy throughout the entire learning and decision-making process. 

“This could be a cornerstone for the next generation of AI, including Generative AI,” said Associate Professor Diep Nguyen.