What Does it Mean for a Language Model to Preserve Privacy? (2202.05520v2)

Abstract: Natural language reflects our private lives and identities, making its privacy concerns as broad as those of real life. LLMs lack the ability to understand the context and sensitivity of text, and tend to memorize phrases present in their training sets. An adversary can exploit this tendency to extract training data. Depending on the nature of the content and the context in which this data was collected, this could violate expectations of privacy. Thus there is a growing interest in techniques for training LLMs that preserve privacy. In this paper, we discuss the mismatch between the narrow assumptions made by popular data protection techniques (data sanitization and differential privacy), and the broadness of natural language and of privacy as a social norm. We argue that existing protection methods cannot guarantee a generic and meaningful notion of privacy for LLMs. We conclude that LLMs should be trained on text data which was explicitly produced for public use.

• The paper reveals that existing privacy techniques fail to capture the contextual complexity of language, risking exposure of sensitive text.
• It demonstrates that data sanitization and differential privacy methods fall short in protecting nuanced information during model training.
• The study advocates using data explicitly intended for public release to better align privacy safeguards with realistic usage expectations.

Insights on Privacy in LLMs

This paper titled "What Does it Mean for a LLM to Preserve Privacy?" examines the complexities of privacy in the context of training LLMs (LMs). It addresses the limitations of current data protection techniques—data sanitization and differential privacy (DP)—highlighting that they do not adequately cater to the nuanced nature of privacy in natural language.

LLMs, pivotal in natural language processing, are trained on extensive datasets. This extensive training raises privacy concerns, given their propensity to memorize phrases from the datasets. These memorized phrases, potentially extracted by adversaries, can lead to privacy violations depending on the data's nature and context.

Key Arguments

The paper articulates several critical arguments that underscore the inadequacies of existing privacy-preserving techniques for LMs:

1. Contextual Complexity of Human Language:
   • Language is inherently contextual, with varying expectations of privacy based on the situation and entities involved. This complexity is challenging to formalize in a privacy-preserving framework.
   • Existing methods do not account for the contextual nuances that govern when information is considered private. The boundaries of what constitutes a secret are blurred, and identifying sensitive information requires understanding the context—an attribute current models lack.
2. Limitations of Data Sanitization:
   • Data sanitization assumes the ability to specify and remove private information efficiently. However, in practice, the format and context-dependent nature of language makes it difficult to identify and sanitize sensitive information adequately.
   • The paper argues that while sanitization methods can remove formatted private information (like social security numbers), their efficacy is limited for less structured data due to the indeterminate borders of textual secrets.
3. Challenges with Differential Privacy:
   • DP provides guarantees by ensuring an algorithm's output does not drastically change with the inclusion or exclusion of a single record, but this is insufficient for language data, where private information can span multiple users.
   • The paper critiques DP for assuming discrete data records without overlapping private information, which is rarely the case with language data.
4. Implications of Public Data Use:
   • Training LMs on publicly accessible data does not mitigate privacy risks because public availability does not equal public intent. Even publicly shared data have contextual privacy expectations that models might violate when memorized.
   • The authors argue for training LMs exclusively on data explicitly intended for public dissemination to meet privacy expectations meaningfully.

Implications and Future Directions

The paper encourages a re-evaluation of what privacy means in the context of LMs. It advocates for a shift away from traditional data protection measures towards developing models only using data intended and consented for public use. This approach promises to respect privacy more rigorously, minimizing the risk of unintended information exposure.

The discourse presented suggests areas for future research. It is imperative to devise novel privacy-preserving methodologies that consider the diffuse nature of secrets in language and the blurred ownership of textual information. Collaborative efforts between legal, ethical, and technical fields will be crucial in framing new guidelines for data use in LMs.

In conclusion, while existing privacy-preserving techniques offer some level of data protection, the paper asserts they fall short of encompassing the broad and nuanced concept of privacy required for language data. It proposes that future advancements in training LMs should prioritize data intentionally made public, emphasizing the need for an evolved understanding and implementation of privacy preservation in AI systems.