Unveiling the Privacy Risks of Pre-Trained Language Models: The PreCurious Framework

Core Concepts

PreCurious framework amplifies privacy risks in fine-tuning pre-trained language models.

Abstract

The PreCurious framework introduces a new attack surface where pre-trained models can be manipulated to escalate privacy risks during fine-tuning. By crafting model initialization, attackers can amplify membership inference and data extraction risks. Different PEFT techniques are explored for their effectiveness in defending against privacy attacks. The study highlights the vulnerabilities of common-sense defenses and the risks associated with downloading pre-trained models from unknown sources.

Stats

"AUC↑ to measure the effectiveness of the attack."
"TPR@FPRα%↑ given a small α to measure the privacy risk."
"pext ↑ of sub-sequence emitted by the target model."
"nnew ↑ indicating new extracted tokens not repeated in Daux."
"vexp ↑ measuring if a targeted secret can be reliably extracted."

Quotes

Key Insights Distilled From

PreCurious

by Ruixuan Liu,... at arxiv.org 03-15-2024

https://arxiv.org/pdf/2403.09562.pdf

Deeper Inquiries

What are the implications of relying solely on common-sense defenses when fine-tuning language models

Relying solely on common-sense defenses when fine-tuning language models can have significant implications for privacy and security. Common-sense defenses may not be sufficient to protect against sophisticated attacks, especially in scenarios where the pre-trained model integrity is compromised. In the context of the PreCurious framework outlined in the provided text, common-sense defenses such as overfitting mitigation, differentially private fine-tuning, and deduplication may not be effective in defending against privacy attacks that manipulate the memorization stage of pre-trained models.
By relying solely on common-sense defenses without a deeper understanding of potential vulnerabilities and attack surfaces like those highlighted by PreCurious, users downloading pre-trained models from unknown sources are at risk of exposing sensitive information during fine-tuning. It is crucial for users to adopt more robust security measures and consider factors beyond basic defense strategies to safeguard their data and mitigate privacy risks effectively.

How does the choice of model initialization impact privacy risks in fine-tuning

The choice of model initialization plays a critical role in determining the level of privacy risks associated with fine-tuning language models. In the experiments conducted using the PreCurious framework, it was observed that crafting model initializations with specific strategies could significantly impact MIA (Membership Inference Attack) effectiveness within a given number of iterations.
Accelerated model initializations pushed towards memorization-only stages resulted in higher privacy risks due to rapid membership inference capabilities. On the other hand, lagging model initializations with inferior performance on domain-specific tasks also increased privacy risks by making it easier for attackers to distinguish between IN-world samples (those present in training data) and OUT-world samples (those not present).
Choosing an appropriate reference model for calibration purposes further influenced adversarial advantage levels during membership inference attacks. The best approach involved selecting just-fit models as both θref (reference model) and θpre (crafted initialization), maximizing attacking effectiveness across various metrics, datasets, and parameter-efficient fine-tuning methods.

How can users mitigate privacy risks when downloading pre-trained models from unknown sources

To mitigate privacy risks when downloading pre-trained models from unknown sources, users should implement several key practices:

Source Verification: Verify the credibility and trustworthiness of sources before downloading any pre-trained models.

Validation Processes: Ensure that downloaded models undergo rigorous validation processes to confirm their integrity before use.

Reference Models: Use well-calibrated reference models for comparison during calibration processes to enhance detection capabilities against maliciously crafted initializations.

Stealthiness Metrics: Monitor stealthiness metrics such as Sgap ↓, Smia ↓, Smem ↓ to detect any suspicious behavior or deviations from expected patterns.

Auditing Practices: Regularly audit released pre-trained models using benign counterparts or known references to identify anomalies or discrepancies indicating potential security threats.

By following these mitigation strategies along with maintaining vigilance while handling pre-trained language models from unverified sources, users can better protect themselves against potential privacy breaches and security vulnerabilities inherent in such scenarios.