Active Privacy Auditing of Supervised Fine-tuned White-Box Language Models: Exposing Privacy Risks with PARSING

Core Concepts

Fine-tuning large language models (LLMs) on specific datasets, even under a white-box setting, poses significant privacy risks, as demonstrated by the effectiveness of the proposed active privacy auditing framework, PARSING.

Abstract

• Bibliographic Information: Sun, Q., Wu, H., & Zhang, X. S. (2024). On Active Privacy Auditing in Supervised Fine-tuning for White-Box Language Models. arXiv preprint arXiv:2411.07070v1.
• Research Objective: This paper introduces PARSING, an active privacy auditing framework designed to identify and quantify privacy leakage risks during the supervised fine-tuning (SFT) of white-box language models (LLMs).
• Methodology: PARSING leverages improved white-box membership inference attacks (MIAs) with novel learning objectives and a two-stage pipeline. It analyzes forward properties (intermediate module outputs) and backward properties (gradients) during fine-tuning to detect privacy vulnerabilities.
• Key Findings:
  • PARSING effectively identifies and quantifies privacy risks across various LLM architectures (GPT-2, Llama2) and NLP tasks.
  • Larger models and complex tasks exhibit higher privacy leakage risks.
  • Longer text lengths correlate with increased vulnerability to privacy breaches.
  • Backward properties (gradients) provide stronger attack capabilities than forward properties.
  • Parameter-efficient fine-tuning techniques can mitigate privacy risks compared to full-parameter fine-tuning.
• Main Conclusions: Fine-tuning LMs, even in a white-box setting, can lead to significant privacy leakage. The effectiveness of PARSING highlights the need for robust privacy-preserving mechanisms in LLM development and deployment.
• Significance: This research significantly contributes to understanding and addressing privacy vulnerabilities in fine-tuned LLMs, crucial for building trust and ensuring responsible AI development.
• Limitations and Future Research: Future research should explore the impact of diverse fine-tuning datasets, dynamic learning environments, and the development of more sophisticated privacy-enhancing techniques for LLM fine-tuning.

Stats

For GPT-2-large, backward attacks reach 73.9% accuracy, while forward attacks peak at 66.4%.
When the fine-tuning accuracy reaches 82.5%, the highest balance accuracy of the attack is 71.7%.
Conversely, at a fine-tuning accuracy of 70.7%, the highest balance attack accuracy is 58.4%, with a forward information attack success rate of 50.9%.
For the PubMed_RCT dataset, the balance accuracy under FFT is approximately 0.766.
When the number of tunable parameters is reduced to 33.6M, the balance accuracy drops to 0.672.
When the number of training parameters is further reduced to below 1M, the balance accuracy drops to just 0.627.

Quotes

"The constrained datasets employed for fine-tuning usually do not match the number of model parameters, resulting in an over-reliance on a limited set of data samples [...] This reliance not only greatly impacts the model’s ability to generalize, but also raises the likelihood of disclosing sensitive details from the training data."
"Thorough privacy evaluations greatly enhance user trust in models and their creators, serving as a crucial element in the sustainable advancement of technology."

Key Insights Distilled From

On Active Privacy Auditing in Supervised Fine-tuning for White-Box Language Models

by Qian Sun, Ha... at arxiv.org 11-12-2024

https://arxiv.org/pdf/2411.07070.pdf

Deeper Inquiries

How can differential privacy techniques be integrated into the fine-tuning process of LLMs to mitigate privacy risks without significantly compromising model utility?

Integrating differential privacy (DP) into the fine-tuning process of Large Language Models (LLMs) presents a delicate balancing act between privacy and utility. Here's a breakdown of potential methods and considerations:
Methods of Integration:

DP-SGD: This involves injecting noise into the gradients during stochastic gradient descent (SGD) optimization.

Challenge: Finding the optimal noise level is crucial. Excessive noise can severely degrade the model's accuracy, especially given the vast number of parameters in LLMs.
Mitigation: Techniques like adaptive clipping and gradient aggregation can help manage the noise-accuracy trade-off.

Objective Perturbation: Adding noise directly to the loss function during fine-tuning.

Challenge: Similar to DP-SGD, finding the right noise magnitude is key.
Mitigation: Exploring different noise distributions (e.g., Gaussian, Laplacian) and carefully tuning hyperparameters can help.

Private Aggregation of Teacher Ensembles (PATE): Training multiple LLMs on disjoint subsets of the private data and then using a "teacher" model to provide aggregated, privacy-preserving predictions.

Advantage: Stronger privacy guarantees.
Challenge: Computationally expensive to train and maintain multiple LLMs.

Preserving Model Utility:

Task-Specific Fine-tuning: Applying DP selectively to sensitive layers or components of the LLM most prone to memorization can help preserve utility on less sensitive tasks.
Pre-training on Public Data: Thorough pre-training on massive public datasets can reduce the LLM's reliance on sensitive data during fine-tuning, making DP more effective.
Hybrid Approaches: Combining DP with other privacy-enhancing techniques like federated learning or homomorphic encryption can offer stronger protection without excessive utility loss.
Ongoing Research:
The field of DP for LLMs is actively evolving. Research into more efficient and less disruptive DP mechanisms tailored to the specific characteristics of LLMs is crucial.

Could the findings on privacy risks associated with fine-tuning be leveraged to develop more robust and secure language models inherently designed to protect user data?

Absolutely, the findings on privacy risks in LLM fine-tuning provide valuable insights for designing inherently more secure models. Here's how:
Architecture and Training Paradigm Shifts:

Privacy-Preserving Architectures: Exploring novel LLM architectures that inherently limit memorization of individual training samples. This could involve:

Decentralized training approaches.
Capsule networks that focus on higher-level representations.
Differential privacy-aware design principles embedded in the model structure.

Adversarial Training: Training LLMs with adversarial examples crafted to mimic Membership Inference Attacks (MIAs) can make them more robust to such attacks.
Regularization Techniques: Incorporating stronger regularization techniques during training to penalize excessive memorization of training data.
Data Handling and Model Access:

Federated Learning: Training LLMs on decentralized data silos without directly accessing raw user data can enhance privacy.
Secure Enclaves: Running LLM fine-tuning within secure hardware enclaves can protect sensitive data from unauthorized access.
Differential Privacy as a Design Constraint: Integrating DP principles throughout the LLM lifecycle, from data collection and pre-processing to model training and deployment.
Beyond Technical Solutions:

Transparency and Explainability: Developing more interpretable LLMs that allow users to understand how their data influences model behavior.
Ethical Frameworks and Regulations: Establishing clear guidelines and regulations for responsible LLM development and deployment, emphasizing user privacy and data security.

What are the ethical implications of developing increasingly powerful language models capable of memorizing and potentially exposing sensitive information, and how can these concerns be addressed through responsible AI practices and regulations?

The development of increasingly powerful LLMs capable of memorizing and potentially exposing sensitive information raises significant ethical concerns:
Ethical Implications:

Privacy Violation: Unintended memorization and potential extraction of sensitive personal information from training data can lead to severe privacy breaches.
Discrimination and Bias: If trained on biased data, LLMs can perpetuate and even amplify existing societal biases, leading to unfair or discriminatory outcomes.
Misinformation and Manipulation: LLMs can be exploited to generate highly convincing fake news, propaganda, or deepfakes, eroding trust and potentially causing harm.
Erosion of Autonomy: The use of LLMs in decision-making systems without proper transparency and accountability can undermine human autonomy and agency.
Addressing Ethical Concerns:
Responsible AI Practices:

Data Governance: Implementing robust data governance frameworks that prioritize data privacy, security, and ethical sourcing.
Bias Mitigation: Developing and deploying techniques to detect and mitigate bias in training data and LLM outputs.
Transparency and Explainability: Making LLM decision-making processes more transparent and understandable to users.
Red Teaming and Auditing: Conducting thorough red teaming exercises and independent audits to identify and address potential vulnerabilities and biases.
Regulations and Governance:

Data Protection Laws: Strengthening and enforcing data protection laws like GDPR and CCPA to regulate the collection, storage, and use of personal data in LLM training.
Algorithmic Accountability: Establishing legal frameworks that hold developers and deployers of LLMs accountable for potential harms caused by their models.
Ethical Guidelines and Standards: Developing industry-wide ethical guidelines and standards for responsible LLM development and deployment.
Public Engagement and Education:

Raising Awareness: Educating the public about the capabilities, limitations, and potential risks of LLMs.
Fostering Dialogue: Encouraging open and inclusive dialogue among researchers, developers, policymakers, and the public to address ethical concerns.
By embracing responsible AI practices and establishing robust regulations, we can strive to harness the immense potential of LLMs while mitigating their ethical risks and safeguarding user privacy.