Cape: Context-Aware Prompt Perturbation Mechanism with Differential Privacy

Understand

Large Language Models (LLMs) have gained significant popularity due to their remarkable capabilities in text understanding and generation.

• However, despite their widespread deployment in inference services such as ChatGPT, concerns about the potential leakage of sensitive user data have arisen.
• Existing solutions primarily rely on privacy-enhancing technologies to mitigate such risks, facing the trade-off among efficiency, privacy, and utility.
• To narrow this gap, we propose Cape, a context-aware prompt perturbation mechanism based on differential privacy, to enable efficient inference with an improved privacy-utility trade-off.