Summary DEPN Detecting and Editing Privacy Neurons in Pretrained Language Models arxiv.org
6,771 words - PDF document - View PDF document
One Line
DEPN algorithm identifies and modifies privacy neurons in language models, effectively minimizing data exposure while maintaining model performance.
Slides
Slide Presentation (10 slides)
Key Points
- DEPN is a framework proposed to detect and edit privacy neurons in pretrained language models.
- The framework consists of three components: a privacy neuron detector, a privacy neuron editor, and a privacy neuron aggregator.
- The privacy neuron detector uses gradient integration to compute the contributions of multiple markers to neuron activations, allowing for the estimation of an overall privacy attribution score for private information.
- The privacy neuron editor sets the activations of selected privacy neurons to zero, effectively erasing the model's memorization of private information.
- DEPN can significantly and efficiently reduce the exposure of private data leakage without affecting model performance.
- In-depth analyses reveal the relationship between model memorization and privacy neurons, indicating that larger models tend to have a higher risk of privacy breaches.
- The framework has limitations, including being more effective in scenarios with a small number of data leaks and depending on the amount of private data to be erased.
- DEPN offers a promising framework for privacy protection in pretrained language models, contributing to model dememorization.
Summaries
20 word summary
DEPN detects and edits privacy neurons in pretrained language models using gradient integration, reducing data exposure without impacting model performance.
63 word summary
DEPN is a framework that detects and edits privacy neurons in pretrained language models. It includes a privacy neuron detector, editor, and aggregator. The detector uses gradient integration to identify neurons contributing to privacy leakage. The editor sets activations of selected privacy neurons to zero, reducing data exposure without impacting model performance. DEPN offers a promising approach to privacy preservation and model dememorization.
154 word summary
DEPN is a framework designed to detect and edit privacy neurons in pretrained language models, aiming to mitigate the risk of data leakage. It consists of three components: a privacy neuron detector, editor, and aggregator. The detector uses gradient integration to estimate a privacy attribution score for identifying neurons contributing to privacy leakage. The editor sets activations of selected privacy neurons to zero, erasing the model's memorization of private information. The aggregator facilitates batch editing by calculating privacy attribution scores for each sequence. Experimental results demonstrate DEPN's effectiveness in reducing private data exposure without impacting model performance. The framework introduces novel methods for privacy protection in pretrained language models and reveals the relationship between model memorization and privacy neurons. However, limitations include reduced effectiveness with a large number of data leaks and dependency on the amount of private data to be erased. Overall, DEPN offers a promising approach to privacy preservation and model dememorization.
431 word summary
DEPN is a framework proposed to detect and edit privacy neurons in pretrained language models. The goal is to reduce the risk of data leakage in large language models, which have been shown to memorize and regurgitate a significant portion of the training data. The framework consists of three components: a privacy neuron detector, a privacy neuron editor, and a privacy neuron aggregator.
The privacy neuron detector uses gradient integration to compute the contributions of multiple markers to neuron activations, allowing for the estimation of an overall privacy attribution score for private information. This score measures the neuron's contribution to the leakage of privacy information. The top privacy neurons with the highest scores are selected for editing.
The privacy neuron editor sets the activations of the selected privacy neurons to zero, effectively erasing the model's memorization of private information. This editing strategy minimizes the flow of information through these neurons.
The privacy neuron aggregator facilitates privacy information editing in batches when processing multiple sentences at the same time. It calculates the privacy attribution score matrix for each sequence in the batch and selects the top privacy neurons based on these scores for editing.
Experimental results show that DEPN can significantly and efficiently reduce the exposure of private data leakage without affecting model performance. It is highly effective in protecting privacy and suitable for scenarios with deep model memorization.
The framework has several contributions. It explores model editing for privacy protection in pretrained language models, providing a new way to address privacy risks. It introduces the privacy neuron detector to locate privacy neurons based on gradient attribution and the privacy neuron editor to dememorize private information. The framework is efficient and robust, as demonstrated by experimental results and analyses.
In-depth analyses further reveal the relationship between model memorization and privacy neurons. The distribution of privacy neurons over layers indicates that as training progresses, the aggregation of privacy neurons in deep layers increases. Larger models tend to have a higher risk of privacy breaches, but the DEPN framework offers better protection for these models.
The framework has some limitations. It is more effective in scenarios with a small number of data leaks and may exhibit weaker performance with a large batch of private data. Additionally, the framework's effectiveness depends on the amount of private data to be erased.
In conclusion, DEPN is a promising framework for privacy protection in pretrained language models. It effectively detects and edits privacy neurons, reducing the risk of data leakage without compromising model performance. The framework offers a new approach to privacy preservation and contributes to model dememorization.