Privacy Issues in Large Language Models: A Survey (2312.06717v4)

Abstract: This is the first survey of the active area of AI research that focuses on privacy issues in LLMs. Specifically, we focus on work that red-teams models to highlight privacy risks, attempts to build privacy into the training or inference process, enables efficient data deletion from trained models to comply with existing privacy regulations, and tries to mitigate copyright issues. Our focus is on summarizing technical research that develops algorithms, proves theorems, and runs empirical evaluations. While there is an extensive body of legal and policy work addressing these challenges from a different angle, that is not the focus of our survey. Nevertheless, these works, along with recent legal developments do inform how these technical problems are formalized, and so we discuss them briefly in Section 1. While we have made our best effort to include all the relevant work, due to the fast moving nature of this research we may have missed some recent work. If we have missed some of your work please contact us, as we will attempt to keep this survey relatively up to date. We are maintaining a repository with the list of papers covered in this survey and any relevant code that was publicly available at https://github.com/safr-ml-lab/survey-LLM.

• The paper reveals that unintended memorization in LLMs increases privacy breaches, particularly with larger models and duplicated data.
• The paper details membership inference and data extraction attacks, advocating robust privacy-enhancing technologies like differential privacy.
• The paper highlights regulatory and copyright challenges, emphasizing the need for scalable machine unlearning and updated legal frameworks.

Privacy Issues in LLMs: A Survey

The paper "Privacy Issues in LLMs: A Survey" offers a comprehensive review of the ongoing research on the privacy challenges associated with LLMs. LLMs, such as those used in ChatGPT, have brought remarkable advancements in natural language processing and have been widely adopted across industries. However, they pose various privacy risks due to their training on extensive datasets, often containing sensitive and copyrighted information.

Key Areas of Investigation

1. Memorization and Privacy Risks: LLMs have a tendency to memorize training data, which can lead to unintended privacy breaches. The paper discusses this phenomenon, coined as "unintended memorization," where models can regurgitate sensitive information verbatim. Studies show that larger model sizes and data duplications exacerbate this issue. Methods such as de-duplication and differential privacy during training are explored as mitigation strategies.
2. Membership Inference and Data Extraction Attacks: These attacks aim to determine whether specific data points were part of the model's training set or to extract training data directly. The authors review various approaches, including threshold and shadow model attacks, and highlight the need for robust defenses in models to protect sensitive data from being uncovered. The implementation of Privacy Enhancing Technologies (PETs) like differential privacy offers promising, albeit complex, solutions.
3. Regulatory Context and Legal Implications: The paper explores current legislative frameworks like GDPR and the emerging legal landscape around AI in the U.S., emphasizing the "Right to Be Forgotten." It points out the challenge of aligning LLM operations with these regulations, especially in cases requiring data deletion from a model post-training. Machine unlearning is introduced as a potential solution, though its scalability remains an issue.
4. Copyright Concerns: The paper addresses how LLMs, through training on copyrighted texts, can reproduce protected content, thus infringing on intellectual property rights. The researchers discuss existing legal doctrines and court cases that may impact how LLM-generated content is viewed under copyright law. They also speculate on the future of copyright as it pertains to AI-created works, considering models' access to and use of copyright material.

Practical and Theoretical Implications

The implications of these privacy concerns are significant both in practice and theory. Practically, they influence how LLMs are deployed and managed, requiring developers to incorporate privacy-preserving techniques and stay abreast of legal developments. Theoretically, they push the boundaries of machine learning research, necessitating new frameworks and algorithms that balance utility and privacy.

Future Developments

The survey speculates on several future developments necessary for aligning LLMs with privacy standards:

• Advancements in Unlearning Techniques: As legislation grows more stringent, developing efficient machine unlearning methods that comply with privacy laws without severely impacting model performance will be crucial.
• Improved Privacy Metrics and Benchmarks: Establishing standardized benchmarks and metrics for evaluating privacy in LLMs can guide the development of more secure models.
• Legal and Ethical Frameworks: As AI technologies advance, updating legal and ethical frameworks to address the unique challenges posed by AI-generated content and its implications will be essential.

In summary, the survey provides a thorough analysis of the privacy issues associated with LLMs, underlining the importance of ongoing research and innovation in both technical solutions and legal frameworks to ensure these powerful tools are used responsibly and ethically.