PrivacySIM: Evaluating LLM Simulation of User Privacy Behavior
James Flemings, Murali Annavaram
原文摘要
Large language models (LLMs) are increasingly used to simulate human behavior, but their ability to simulate individual privacy decisions is not well understood. In this paper, we evaluate whether a core set of user persona attributes can drive LLMs to simulate individual-level privacy behavior across various contexts. We introduce PrivacySIM, an evaluation suite that benchmarks LLM simulation of user privacy behavior against the ground-truth responses of 1,000 real users. These users are drawn from five published user studies on privacy behavior spanning LLM healthcare consultations, conversational agents, and chatbots. Drawing on these user studies and prior work in privacy behavior, we hypothesize three persona facets as plausible predictors of privacy decision-making: demographics, previous experiences, and stated privacy attitudes. We condition nine frontier LLMs on subsets of these three facets and measure how often each model's response to a data-sharing scenario matches the user's actual response. Our findings show that (1) zero-shot conditioning on these facets consistently improves simulation quality, but the performance of the best model (40.4% accuracy) suggests that richer and more nuanced persona facets are needed to reliably align with users' mental models for privacy decisions. (2) A user's stated privacy attitudes alone are often not the best predictor because they can diverge from the user's actual privacy behavior. (3) Users with high AI/chatbot experience are the most challenging to simulate. PrivacySIM is a first step toward understanding and improving the capabilities of LLMs to simulate user privacy decisions. We release PrivacySIM to enable further evaluation of LLM privacy simulation.
cs.CR · cs.LG