Trust No Bot: Discovering Personal Disclosures in Human-LLM Conversations in the Wild (2407.11438v2)

Abstract: Measuring personal disclosures made in human-chatbot interactions can provide a better understanding of users' AI literacy and facilitate privacy research for LLMs. We run an extensive, fine-grained analysis on the personal disclosures made by real users to commercial GPT models, investigating the leakage of personally identifiable and sensitive information. To understand the contexts in which users disclose to chatbots, we develop a taxonomy of tasks and sensitive topics, based on qualitative and quantitative analysis of naturally occurring conversations. We discuss these potential privacy harms and observe that: (1) personally identifiable information (PII) appears in unexpected contexts such as in translation or code editing (48% and 16% of the time, respectively) and (2) PII detection alone is insufficient to capture the sensitive topics that are common in human-chatbot interactions, such as detailed sexual preferences or specific drug use habits. We believe that these high disclosure rates are of significant importance for researchers and data curators, and we call for the design of appropriate nudging mechanisms to help users moderate their interactions.

• The paper reveals that over 70% of human-LLM interactions contain personal disclosures, exposing significant privacy vulnerabilities.
• It employs a mixed-methods analysis on one million WildChat interactions to classify sensitive topics and assess detection reliability.
• The study advocates for advanced privacy-preserving measures, including nudging mechanisms and improved detection systems for safer LLM usage.

Exploring User Privacy in LLM Interactions

The paper "Trust No Bot: Discovering Personal Disclosures in Human-LLM Conversations in the Wild" presents an in-depth empirical evaluation of privacy concerns in LLM-human interactions. Using the WildChat dataset, the authors explore the nature and frequency of personal disclosures in conversations between users and chatbots powered by LLMs such as ChatGPT. This research is pivotal in understanding the privacy implications surrounding LLM usage and proposes mitigative strategies to safeguard user data.

Core Contributions and Methodology

The authors utilize the WildChat dataset, a collection of one million user-LLM interactions, to analyze how users engage with chatbots and reveal sensitive information. The paper identifies the types of information users commonly disclose, such as personal identifiable information (PII) and other sensitive categories like sexual preferences and drug use. The researchers introduce a taxonomy of tasks and topics occurring in these conversations, aligning with a detailed analysis of contexts in which these revelations occur.

Three key research questions guide this exploration:

1. Types of sensitive information shared.
2. Frequency and detection reliability of information leakage.
3. Situational contexts fostering varying levels of sensitivity disclosure.

To address these questions, the authors employ a combination of qualitative and quantitative methodologies. The analysis includes automatic detection of PII and annotations validated through human feedback. Notably, the authors highlight instances where traditional PII detection systems fail, capturing only a fraction of sensitive topics mentioned, thus necessitating a broader analytical approach to privacy concerns.

Quantitative Findings

The research uncovers significant findings:

• Over 70% of queries in the WildChat dataset contain detected PII, and approximately 15% encompass sensitive subjects not traditionally categorized as PII.
• Specific tasks, such as translation queries, unexpectedly include high rates of disclosure, with nearly 50% containing PII.
• The exploration reveals limitations in existing PII detection mechanisms, prompting a call for improved systems capable of identifying a broader range of sensitive disclosures.

Implications and Future Directions

The findings highlight substantial privacy risks associated with LLM usage, due to inadvertent data leaks and unintentional disclosures by users. The authors advocate for the development of nudging mechanisms that alert users about potential privacy risks during interactions. Moreover, they underline the necessity for increased transparency from companies deploying these chatbots, recommending the integration of privacy-preserving techniques such as differential privacy and user-centric design.

From a research standpoint, the paper prompts further inquiry into privacy-enhancing technologies and methodologies in AI. It raises awareness about the ethical responsibilities of both developers and researchers engaged in deploying AI systems. Future explorations may focus on developing local, private models that minimize data sharing while preserving the functionality and benefits of LLMs.

Conclusion

In conclusion, the paper "Trust No Bot" offers a comprehensive evaluation of privacy vulnerabilities inherent in human-LLM interactions. By illuminating the types and contexts of sensitive information disclosure, this work encourages best practices in AI system design to bolster user privacy. The research serves as a critical resource for the design of future LLM systems that prioritize user privacy and addresses the ethical implications of AI technology deployment.