Data Privacy and Large Language Models

Report on Current Developments in Data Privacy and Large Language Models

General Direction of the Field

The recent advancements in the field of data privacy, particularly in the context of Large Language Models (LLMs), are marked by a shift towards more nuanced and context-aware approaches. Researchers are increasingly focusing on developing methodologies that not only detect and mitigate privacy risks but also integrate these solutions into the core design of LLMs. This trend is driven by the recognition that traditional privacy measures, often applied as afterthoughts or patchworks, are insufficient in the dynamic and complex environment of AI and NLP technologies.

One of the significant developments is the exploration of context-centric privacy measures, which leverage theories like Contextual Integrity (CI) to better align privacy protections with users' actual concerns and social contexts. This approach moves beyond simplistic pattern matching and instead formulates privacy as a reasoning problem, enhancing the relevance and effectiveness of privacy safeguards.

Another notable trend is the emphasis on access control and data leakage prevention in LLM training and deployment. Techniques such as double model balancing and adjusted influence functions are being developed to ensure that sensitive information is protected while maintaining the utility of LLMs. These methods address the challenges posed by access-controlled datasets and the inherent risks of privacy leakage during model training.

Furthermore, there is a growing awareness of the privacy implications in LLM app ecosystems. Researchers are conducting in-depth investigations into data practices of LLM apps, particularly focusing on third-party interactions and data collection. This research aims to bring transparency and accountability to the data handling practices of LLM platforms, which are crucial for safeguarding user privacy.

Noteworthy Papers

  • Privacy Checklist: Privacy Violation Detection Grounding on Contextual Integrity Theory: This paper introduces a comprehensive checklist for privacy detection, significantly advancing context-centric privacy research by integrating large language models and expert annotations across multiple ontologies.
  • DOMBA: Double Model Balancing for Access-Controlled Language Models via Minimum-Bounded Aggregation: Proposing a novel approach for training LLMs with access control, DOMBA ensures high utility and security by aggregating probability distributions of models trained on different access levels.
  • LLM-PBE: Assessing Data Privacy in Large Language Models: Introducing a toolkit for systematic evaluation of data privacy risks in LLMs, LLM-PBE provides a comprehensive framework for analyzing privacy across the entire lifecycle of LLMs, addressing a critical gap in the literature.

These papers represent pioneering efforts that not only advance the field but also set new standards for future research in data privacy and LLM development.

Sources

State surveillance in the digital age: Factors associated with citizens' attitudes towards trust registers

Privacy Checklist: Privacy Violation Detection Grounding on Contextual Integrity Theory

DOMBA: Double Model Balancing for Access-Controlled Language Models via Minimum-Bounded Aggregation

Tracing Privacy Leakage of Language Models to Training Data via Adjusted Influence Functions

Inside the Black Box: Detecting Data Leakage in Pre-trained Language Encoders

Randomization Techniques to Mitigate the Risk of Copyright Infringement

Data Exposure from LLM Apps: An In-depth Investigation of OpenAI's GPTs

LLM-PBE: Assessing Data Privacy in Large Language Models