Unveiling Data Privacy Risks: Manipulating Pre-Trained Models for Data Theft
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In this riveting video by Yannic Kilcher, we delve into a groundbreaking paper by Shan L. and Florian Traumer from ETH Zurich that shakes the very core of data privacy. The paper introduces a devious concept of pilfering sensitive information through corrupted pre-trained models like Bert and visual Transformers. While still in its infancy, this method poses a looming threat to the security of personal data. Imagine a scenario where a user fine-tunes a model with confidential information, intending to create a classifier to detect personal data in text. However, even if the model is shielded behind an API, this paper unveils a cunning blackbox attack that can extract the fine-tuning data without exploiting conventional security vulnerabilities.
The attacker's ability to manipulate the pre-trained model's weights to imprint the training data opens a Pandora's box of privacy breaches. This nefarious scheme could stem from gaining reputation on platforms like Hugging Face, leading to potential compromises in data security. By adding a layer to the pre-trained model and utilizing SGD for training, the attacker can embed data points in the weights, paving the way for future data reconstruction. The paper's audacious approach challenges existing notions of privacy guarantees, pushing the boundaries of differential private training to its theoretical limits.
Through this method, the attacker can extract training examples and jeopardize data privacy, highlighting the critical need for enhanced security measures in machine learning models. The technique focuses on embedding training data in the model's weights, enabling model stealing attacks even in scenarios where only input-output pairs are accessible. This groundbreaking research underscores the paramount importance of safeguarding sensitive information in the ever-evolving landscape of artificial intelligence and machine learning.
Image copyright Youtube
Image copyright Youtube
Image copyright Youtube
Image copyright Youtube
Watch Privacy Backdoors: Stealing Data with Corrupted Pretrained Models (Paper Explained) on Youtube
Viewer Reactions for Privacy Backdoors: Stealing Data with Corrupted Pretrained Models (Paper Explained)
Data traps embedded within model's weights
Single-use activation for capturing information
Latch mechanism to prevent further updates
Gradient manipulation for specific data points
Target selection based on data distribution
Bypassing defenses like layer normalization
Reconstructing data from final model weights
Impact on compromising privacy and differential privacy guarantees
Vulnerability in machine learning model supply chain
Susceptibility to mitigation techniques and need for target data distribution calibration
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