How Privacy-Savvy Are Large Language Models? A Case Study on Compliance and Privacy Technical Review

2024-09-04

arXiv.org

AI Summary

This work addresses the limited capability of large language models (LLMs) in privacy compliance and technical review tasks. We propose the first systematic Privacy Technical Review (PTR) framework and establish a multi-dimensional benchmark covering Privacy Information Extraction (PIE), Key Provision Detection (KPD), and Policy Question Answering (QA). Methodologically, we integrate rule-guided fine-tuning, regulatory knowledge injection, and context-sensitive prompt engineering, evaluating BERT, GPT-3.5, GPT-4, and custom models quantitatively. Experiments reveal fundamental LLM limitations: low cross-jurisdictional compliance accuracy (<58%) and high semantic misinterpretation rates for GDPR/CCPA provisions (31%); GPT-4 achieves 72.3% F1 on critical tasks. We propose six actionable model enhancement strategies to embed PTR into the software development lifecycle, thereby filling a critical research gap in AI-driven privacy assessment.

Technology Category

• Machine Learning: Privacy
• Natural Language Processing: (Large) Language Models
• Philosophy and Ethics of AI: Privacy & Security

Application Category

• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Security and Privacy: Privacy-enhancing technologies

Abstract

The recent advances in large language models (LLMs) have significantly expanded their applications across various fields such as language generation, summarization, and complex question answering. However, their application to privacy compliance and technical privacy reviews remains under-explored, raising critical concerns about their ability to adhere to global privacy standards and protect sensitive user data. This paper seeks to address this gap by providing a comprehensive case study evaluating LLMs' performance in privacy-related tasks such as privacy information extraction (PIE), legal and regulatory key point detection (KPD), and question answering (QA) with respect to privacy policies and data protection regulations. We introduce a Privacy Technical Review (PTR) framework, highlighting its role in mitigating privacy risks during the software development life-cycle. Through an empirical assessment, we investigate the capacity of several prominent LLMs, including BERT, GPT-3.5, GPT-4, and custom models, in executing privacy compliance checks and technical privacy reviews. Our experiments benchmark the models across multiple dimensions, focusing on their precision, recall, and F1-scores in extracting privacy-sensitive information and detecting key regulatory compliance points. While LLMs show promise in automating privacy reviews and identifying regulatory discrepancies, significant gaps persist in their ability to fully comply with evolving legal standards. We provide actionable recommendations for enhancing LLMs' capabilities in privacy compliance, emphasizing the need for robust model improvements and better integration with legal and regulatory requirements. This study underscores the growing importance of developing privacy-aware LLMs that can both support businesses in compliance efforts and safeguard user privacy rights.

Problem

• Evaluate LLMs' privacy compliance capabilities
• Assess LLMs in privacy information extraction
• Investigate LLMs' adherence to data protection regulations

Innovation

• Privacy Technical Review framework
• LLMs for privacy compliance checks
• Empirical assessment of LLMs