Heng Jin

Intermediate activations in split LLM fine-tuning trivially leak raw training prompts past standard privacy defenses, but a learned obfuscation pipeline closes this leakage vector without destroying model utility.

A malicious parameter server can exploit parameter-efficient fine-tuning to implant a privacy backdoor that reconstructs training data with alarming accuracy.

Bridge the trust gap in cloud-based LLM services with AFTUNE, a practical framework that lets you audit proprietary fine-tuning and inference without prohibitive overhead.