Privacy Backdoors: Amplifying Membership Inference Attacks through Poisoning Pre-trained Models

Concetti Chiave

Adversaries can poison pre-trained models to significantly increase the success rate of membership inference attacks, even when victims fine-tune the models using their own private datasets.

Sintesi

The paper introduces a new type of backdoor attack, called a "privacy backdoor", which aims to amplify the privacy leakage that arises when fine-tuning a pre-trained model. The key idea is to poison the pre-trained model by modifying its weights so that the loss on certain target data points becomes anomalous. This creates a clear distinction between the losses of data points that are included in the fine-tuning dataset and those that are not, significantly boosting the success rate of membership inference attacks.

The authors conduct extensive experiments on various datasets and models, including vision-language models (CLIP) and large language models. They demonstrate the broad applicability and effectiveness of such an attack, and also carry out multiple ablation studies to analyze the impact of different fine-tuning methods and inference strategies.

The results highlight a critical privacy concern within the machine learning community and call for a reevaluation of safety protocols in the use of open-source pre-trained models. The authors emphasize the need for practitioners to exercise increased caution and adopt more thorough validation processes when utilizing these models, as the security of a model should not be presumed safe based solely on its availability from a well-regarded source.

Statistiche

The paper presents several key statistics to support the authors' findings:

For CLIP models on the ImageNet dataset, the true positive rate at 1% false positive (TPR@1%FPR) improved from 0.188 to 0.503 with the poisoning attack.
For large language models on the ai4Privacy dataset, the TPR@1%FPR increased from 0.049 to 0.874 with the poisoning attack.
The poisoning attack had minimal impact on the model's performance, with only a 1-2% decrease in test accuracy for CLIP models.
For large language models, the poisoning attack did not increase the validation loss before or after fine-tuning.

Citazioni

"Our findings highlight a critical privacy concern within the machine learning community and call for a reevaluation of safety protocols in the use of open-source pre-trained models."
"The security of a model should not be presumed safe based solely on its availability from a well-regarded source."

Approfondimenti chiave tratti da

Privacy Backdoors

by Yuxin Wen,Le... alle arxiv.org 04-02-2024

https://arxiv.org/pdf/2404.01231.pdf

Domande più approfondite

How can the machine learning community develop robust techniques to verify the integrity of pre-trained models before using them in downstream applications

To verify the integrity of pre-trained models, the machine learning community can implement several robust techniques. One approach is to establish a standardized verification process that includes checksums or digital signatures for model files. By providing these verification mechanisms, users can confirm the authenticity and integrity of the downloaded models before use. Additionally, the community can promote transparency by encouraging model developers to release detailed documentation on the training data, architecture, and training process. This transparency allows users to assess the model's trustworthiness and potential vulnerabilities. Furthermore, conducting thorough model audits and validation tests, such as adversarial testing and sensitivity analysis, can help identify any hidden vulnerabilities or backdoors in the pre-trained models. Collaborative efforts within the community to share best practices, tools, and resources for model verification can also enhance the overall security and reliability of pre-trained models.

What are the potential countermeasures that can be employed to mitigate the impact of privacy backdoor attacks, and how effective are they in practice

Countermeasures to mitigate the impact of privacy backdoor attacks include implementing strict access controls and data privacy policies to limit the exposure of sensitive training data. Regular security audits and penetration testing can help detect and address vulnerabilities in pre-trained models. Employing differential privacy techniques during model training can add noise to the training data, making it harder for attackers to extract sensitive information. Model watermarking, where unique identifiers are embedded in the model outputs, can help track and identify potential data leaks. Additionally, using federated learning approaches can distribute the training process across multiple devices, reducing the risk of centralized data exposure. While these countermeasures are effective in practice, a combination of multiple strategies tailored to specific use cases is often necessary to provide comprehensive protection against privacy backdoor attacks.

Given the broader implications of this work, how might the insights from this study inform the development of more secure and privacy-preserving machine learning systems in the future

The insights from this study can inform the development of more secure and privacy-preserving machine learning systems by highlighting the importance of rigorous model validation and verification processes. By emphasizing the need for transparency, accountability, and robust security measures in the machine learning community, this study can drive the adoption of best practices for model development and deployment. The findings can also inspire the creation of standardized guidelines and protocols for verifying the integrity of pre-trained models, ultimately enhancing the trustworthiness and reliability of AI systems. Furthermore, the study underscores the significance of ongoing research and innovation in privacy-preserving techniques, encouraging the exploration of advanced methods such as homomorphic encryption, secure multi-party computation, and differential privacy to safeguard sensitive data in machine learning applications.