ProPILE: Probing Privacy Leakage in Large Language Models
Siwon Kim Sangdoo Yun Hwaran Lee Martin Gubri Sungroh Yoon Seong Joon Oh
The rapid advancement and widespread use of large language models (LLMs) have
raised significant concerns regarding the potential leakage of personally
identifiable information (PII). These models are often trained on vast
quantities of web-collected data, which may inadvertently include sensitive
personal data. This paper presents ProPILE, a novel probing tool designed to
empower data subjects, or the owners of the PII, with awareness of potential
PII leakage in LLM-based services. ProPILE lets data subjects formulate prompts
based on their own PII to evaluate the level of privacy intrusion in LLMs. We
demonstrate its application on the OPT-1.3B model trained on the publicly
available Pile dataset. We show how hypothetical data subjects may assess the
likelihood of their PII being included in the Pile dataset being revealed.
ProPILE can also be leveraged by LLM service providers to effectively evaluate
their own levels of PII leakage with more powerful prompts specifically tuned
for their in-house models. This tool represents a pioneering step towards
empowering the data subjects for their awareness and control over their own
data on the web.