Can Language Models be Instructed to Protect Personal Information? (2310.02224v1)

Abstract: Large multimodal LLMs have proven transformative in numerous applications. However, these models have been shown to memorize and leak pre-training data, raising serious user privacy and information security concerns. While data leaks should be prevented, it is also crucial to examine the trade-off between the privacy protection and model utility of proposed approaches. In this paper, we introduce PrivQA -- a multimodal benchmark to assess this privacy/utility trade-off when a model is instructed to protect specific categories of personal information in a simulated scenario. We also propose a technique to iteratively self-moderate responses, which significantly improves privacy. However, through a series of red-teaming experiments, we find that adversaries can also easily circumvent these protections with simple jailbreaking methods through textual and/or image inputs. We believe PrivQA has the potential to support the development of new models with improved privacy protections, as well as the adversarial robustness of these protections. We release the entire PrivQA dataset at https://LLM-access-control.github.io/.

Evaluating the Efficacy of LLMs in Protecting Personal Information with PrivQA

Introduction

The pervasive use of LLMs raises significant privacy concerns due to their potential to memorize and leak personal information— a challenge that has become increasingly urgent with multimodal models like GPT-4 and Flamingo. This issue not only jeopardizes user privacy but also restricts the integration of LLMs in applications involving sensitive data. To explore the balance between privacy protection and model utility, this paper introduces PrivQA, a multimodal benchmark designed to evaluate the ability of LLMs to adhere to access control instructions intended to protect personal information. Through comprehensive experiments, including red-teaming and self-moderation techniques, the authors shed light on the limitations and potential of instructing LLMs for privacy preservation.

Privacy vs. Utility Trade-off

The core of the paper revolves around the trade-off between privacy protections and the utility of LLMs. Previous approaches to mitigate data leakage have presented an "alignment tax," affecting model performance and operational practicality. The paper critiques these methods for their significant degradation in model performance when applied to more realistic privacy control scenarios. On the other hand, reinforcement learning from human feedback (RLHF) and access control instructions emerge as promising, though not entirely effective, strategies for directing model behavior to protect privacy.

PrivQA Benchmark

PrivQA, a novel benchmark comprising textual and visual question-answering tasks, is introduced to systematically evaluate how well models can protect private information while maintaining their utility. The tasks are designed around two categories: Protected Populations and Protected Information, motivated by the General Data Protection Regulation (GDPR). The benchmark is crafted to avoid the pitfalls of using real-world private data, thus making it reproducible and safe for widespread use without sacrificing user privacy.

Empirical Evaluations and Findings

The evaluation of models on the PrivQA benchmark revealed several critical insights:

• Access Control Instructions Inefficacy: Initial experiments demonstrated the ineffectiveness of simple access control instructions across different information types, with only marginal success in preventing privacy leaks.
• Self-Moderation Technique: A proposed self-moderation technique significantly improved the protection scores, showcasing the potential of models to selectively respond to queries based on privacy guidelines. However, this method also revealed biases, particularly against less well-known individuals or those belonging to minority groups.
• Adversarial Robustness: Through red-teaming experiments, the paper highlighted the vulnerability of LLMs to adversarial attacks aimed at circumventing privacy protections. Text and visual prompt injections were notably effective, raising serious concerns about the models' ability to safeguard against determined adversaries.

Theoretical and Practical Implications

The findings underscore the complexity of instructing LLMs to protect personal information in a way that does not compromise their utility. Theoretical advancements in understanding model behavior, in light of privacy instructions, are imperative for future AI safety research. Pragmatically, the paper advocates for a nuanced approach to model development, where privacy considerations are integrated into the fabric of LLMs, rather than applied as afterthoughts.

Future Directions

Looking forward, the development of LLMs with built-in privacy protection mechanisms (such as the recently released GPT4V by OpenAI) appears promising. However, this paper makes it clear that achieving robust privacy protection is a multifaceted challenge that extends beyond technical fixes. It demands a concerted effort to understand the limitations of current models, innovate on self-moderation techniques, and develop robust defenses against adversarial attacks. Future research should also prioritize mitigating the biases uncovered by this paper, ensuring equitable privacy protection across all user groups.

In summary, this paper presents a comprehensive exploration into the capabilities and limitations of LLMs in protecting personal information, offering valuable insights and laying a foundation for future advancements in the field of AI privacy and security.