FHE-Agent: Automating CKKS Configuration for Practical Encrypted Inference via an LLM-Guided Agentic Framework
PositiveArtificial Intelligence
- FHE-Agent has been introduced as an innovative framework designed to automate the configuration of Fully Homomorphic Encryption (FHE) using the CKKS scheme, addressing the complexities that typically hinder its practical deployment in privacy-preserving machine learning as a service (MLaaS). The framework integrates a Large Language Model (LLM) to streamline the configuration process, making it more accessible to practitioners without deep cryptographic expertise.
- This development is significant as it reduces the reliance on fixed heuristics that often lead to inefficient configurations in FHE applications. By automating the expert reasoning process, FHE-Agent enhances the feasibility of deploying encrypted inference in real-world scenarios, potentially broadening the adoption of privacy-preserving technologies in various sectors.
- The emergence of LLM-driven frameworks like FHE-Agent reflects a broader trend towards democratizing advanced technologies, allowing users with limited technical skills to leverage complex systems. This shift raises important discussions about the balance between accessibility and security, particularly as multi-agent systems and LLMs become more prevalent in software development and cyber defense, highlighting the need for robust safeguards against vulnerabilities.
— via World Pulse Now AI Editorial System
