Large Language Models: A New Approach for Privacy Policy Analysis at Scale

TL;DR

This paper demonstrates that Large Language Models like ChatGPT and Llama 2 can effectively automate privacy policy analysis at scale, outperforming traditional NLP methods in accuracy, cost, and efficiency.

Contribution

It introduces a novel application of LLMs for privacy policy analysis, providing guidance on prompt design and validating performance with benchmark datasets.

Findings

F1 score exceeds 93% on benchmark datasets

Reduces costs and processing times compared to traditional methods

Requires less technical expertise for implementation

Abstract

The number and dynamic nature of web and mobile applications presents significant challenges for assessing their compliance with data protection laws. In this context, symbolic and statistical Natural Language Processing (NLP) techniques have been employed for the automated analysis of these systems' privacy policies. However, these techniques typically require labor-intensive and potentially error-prone manually annotated datasets for training and validation. This research proposes the application of Large Language Models (LLMs) as an alternative for effectively and efficiently extracting privacy practices from privacy policies at scale. Particularly, we leverage well-known LLMs such as ChatGPT and Llama 2, and offer guidance on the optimal design of prompts, parameters, and models, incorporating advanced strategies such as few-shot learning. We further illustrate its capability to detect detailed and varied privacy practices accurately. Using several renowned datasets in the domain as a benchmark, our evaluation validates its exceptional performance, achieving an F1 score exceeding 93%. Besides, it does so with reduced costs, faster processing times, and fewer technical knowledge requirements. Consequently, we advocate for LLM-based solutions as a sound alternative to traditional NLP techniques for the automated analysis of privacy policies at scale.