Exposing Personal Attributes: How Large Language Models Can Infer Sensitive Information from Text

Core Concepts

Current state-of-the-art large language models can infer a wide range of personal attributes, including location, income, sex, and more, from unstructured text with high accuracy and at a fraction of the cost of human profilers.

Abstract

The paper presents a comprehensive study on the capabilities of pre-trained large language models (LLMs) to infer personal attributes from text. Key findings:

LLMs can infer a wide range of personal attributes (e.g., location, income, sex) from unstructured text with high accuracy (up to 85% top-1, 95.8% top-3) and at a fraction of the cost and time required by human labelers.
This poses a significant privacy threat as LLMs can be used to automatically profile individuals from large collections of online text, even when the text is anonymized using commercial tools.
The authors demonstrate that current mitigations, such as text anonymization and model alignment, are insufficient to protect against these privacy-invasive inferences.
They also introduce the emerging threat of adversarial chatbots that can steer conversations to extract personal information from users.
The findings highlight the need for stronger privacy protections beyond just preventing memorization of training data, as LLMs' inference capabilities can violate user privacy in previously unattainable ways.

Stats

"there is this nasty intersection on my commute, I always get stuck there waiting for a hook turn"
"I'm a 47-year-old female living in Melbourne, Australia. I work as a software engineer and my annual income is around $80,000."
"I just got back from a trip to visit my family in New York. It was great to see them after so long!"

Quotes

"Current privacy research on large language models (LLMs) primarily focuses on the issue of extracting memorized training data. At the same time, models' inference capabilities have increased drastically. This raises the key question of whether current LLMs could violate individuals' privacy by inferring personal attributes from text given at inference time."
"LLMs that can infer some of these attributes from unstructured excerpts found on the internet could be used to identify the actual person using additional publicly available information (e.g., voter records in the USA). This would allow a malicious actor to link highly personal information inferred from posts (e.g., mental health status) to an actual person and use it for undesirable or illegal activities like targeted political campaigns, automated profiling, or stalking."

Key Insights Distilled From

Beyond Memorization: Violating Privacy Via Inference with Large Language Models

by Robi... at arxiv.org 05-07-2024

https://arxiv.org/pdf/2310.07298.pdf

Deeper Inquiries

How can we develop stronger text anonymization techniques that can keep up with the rapidly increasing inference capabilities of large language models?

To develop stronger text anonymization techniques that can effectively counter the rapidly increasing inference capabilities of large language models (LLMs), several strategies can be employed:

Contextual Understanding: Text anonymization tools should be designed to have a deep understanding of the context in which the information is presented. This includes recognizing subtle cues, references, and implications within the text that could reveal personal attributes.

Advanced Natural Language Processing (NLP) Techniques: Leveraging advanced NLP techniques such as entity recognition, sentiment analysis, and context-aware processing can enhance the effectiveness of text anonymization. These techniques can help identify and mask sensitive information more accurately.

Adversarial Training: By training text anonymization models against adversarial attacks from LLMs, developers can improve the robustness of the anonymization process. This involves exposing the anonymization tool to various scenarios where LLMs attempt to infer personal attributes and adjusting the tool's algorithms accordingly.

Multi-layered Anonymization: Implementing a multi-layered approach to anonymization can involve not only masking direct references to personal attributes but also considering indirect references, implications, and context that could lead to attribute inference.

Continuous Learning and Updating: Text anonymization tools should be continuously updated and trained on new data to adapt to evolving language patterns, new inference techniques, and emerging privacy threats posed by LLMs.

Collaboration and Research: Collaboration between researchers, developers, and privacy experts is essential to stay ahead of the curve in developing effective text anonymization techniques. Research initiatives focusing on privacy-preserving technologies can drive innovation in this area.

By implementing these strategies and incorporating cutting-edge technologies, we can develop stronger text anonymization techniques that can effectively mitigate the privacy risks associated with the inference capabilities of large language models.

What are the potential societal impacts of malicious actors using LLMs to profile individuals at scale, and how can we mitigate these risks?

The potential societal impacts of malicious actors using LLMs to profile individuals at scale are significant and far-reaching:

Privacy Violations: Malicious actors can extract sensitive personal information, such as location, income, gender, and more, from unstructured text, leading to severe privacy violations for individuals.

Identity Theft: Inferences made by LLMs can be used to piece together individuals' identities, making them vulnerable to identity theft, fraud, and other malicious activities.

Targeted Manipulation: Profiling individuals at scale enables targeted manipulation, such as personalized scams, misinformation campaigns, and psychological manipulation, posing a threat to societal trust and stability.

Discrimination and Bias: The use of inferred personal attributes for decision-making processes can perpetuate discrimination and bias, impacting individuals' opportunities and rights.

To mitigate these risks, several measures can be taken:

Regulatory Frameworks: Implementing robust data protection regulations and privacy laws to govern the collection, storage, and use of personal data can help safeguard individuals' privacy rights.

Ethical Guidelines: Establishing ethical guidelines for the responsible use of LLMs and data inference techniques can promote transparency, accountability, and fairness in profiling practices.

Enhanced Security Measures: Strengthening cybersecurity measures to prevent unauthorized access to personal data and sensitive information is crucial in mitigating the risks of malicious profiling.

Education and Awareness: Raising awareness among individuals about the risks of data profiling and the importance of safeguarding personal information can empower them to protect their privacy.

Technological Safeguards: Developing and deploying privacy-preserving technologies, such as differential privacy, federated learning, and secure multi-party computation, can help protect individuals' data during inference processes.

By implementing a combination of regulatory, ethical, technological, and educational measures, we can mitigate the societal impacts of malicious profiling using LLMs and uphold individuals' privacy rights and data protection.

Given the broad range of personal attributes that can be inferred, how might this change our understanding of privacy and data protection in the age of large language models?

The ability of large language models (LLMs) to infer a broad range of personal attributes from unstructured text has profound implications for privacy and data protection:

Granular Privacy Concerns: The extensive range of personal attributes that can be inferred, including location, income, gender, and more, raises granular privacy concerns as even subtle cues in text can reveal sensitive information.

Contextual Privacy: The context in which personal attributes are inferred becomes crucial in understanding privacy risks. LLMs can extract nuanced details from text, highlighting the importance of context-aware privacy protection measures.

Data Minimization: With the potential for LLMs to infer a multitude of personal attributes, the principle of data minimization becomes critical. Organizations and individuals must limit the collection and sharing of unnecessary personal data to mitigate privacy risks.

Informed Consent: In the age of LLMs, obtaining informed consent for data collection and processing becomes more complex. Individuals need to be aware of the potential for detailed attribute inference and its implications on their privacy.

Algorithmic Accountability: The use of LLMs for attribute inference necessitates greater algorithmic accountability. Transparency in how inferences are made, the data used, and the potential impacts on individuals' privacy is essential for maintaining trust.

Dynamic Privacy Policies: Privacy policies and regulations must evolve to address the nuanced privacy risks posed by LLMs. Adaptive and dynamic privacy frameworks can better protect individuals' data in the face of advanced inference capabilities.

In conclusion, the broad range of personal attributes that can be inferred by LLMs reshapes our understanding of privacy and data protection, emphasizing the need for context-aware, granular, and adaptive privacy measures to safeguard individuals' privacy rights in the era of large language models.