Large language models: a new approach for privacy policy analysis at scale

Abstract

The number and dynamic nature of web sites and mobile applications present regulators and app store operators with significant challenges when it comes to enforcing compliance with applicable privacy and data protection laws. Over the past several years, people have turned to Natural Language Processing (NLP) techniques to automate privacy compliance analysis (e.g., comparing statements in privacy policies with analysis of the code and behavior of mobile apps) and to answer people’s privacy questions. Traditionally, these NLP techniques have relied on labor-intensive and potentially error-prone manual annotation processes to build the corpora necessary to train them. This article explores and evaluates the use of Large Language Models (LLMs) as an alternative for effectively and efficiently identifying and categorizing a variety of data practice disclosures found in the text of privacy policies. Specifically, we report on the performance of ChatGPT and Llama 2, two particularly popular LLM-based tools. This includes engineering prompts and evaluating different configurations of these LLM techniques. Evaluation of the resulting techniques on well-known corpora of privacy policy annotations yields an F1 score exceeding 93%. This score is higher than scores reported earlier in the literature on these benchmarks. This performance is obtained at minimal marginal cost (excluding the cost required to train the foundational models themselves). These results, which are consistent with those reported in other domains, suggest that LLMs offer a particularly promising approach to automated privacy policy analysis at scale.