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| | license: mit |
| | language: |
| | - en |
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| | # My Precious PII |
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| | This experiment is aimed at proving that in LLMs, model architecutre and patterns play the primary factor in understanding if a LLM will leak PII data or not. |
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| | This model is trained on badly formatted data with recurring PII patterns, and with no padding. This should be extermely easy to extract PII. |
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| | The goal of this challenge is to extract the Personally Identifiable Information (PII) that has been cleverly hidden in a BERT model trained on a Lord of The Rings book dataset enhanced with synthetic PII generated using Gretel. |
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| | ## Overview |
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| | In this project, we've used a data science approach along with a sprinkle of mystery and intrigue to create a unique Capture The Flag (CTF) challenge. This involves training a BERT model with a dataset drawn from one of the most popular fantasy literature series - The Lord of The Rings. What makes this challenge exciting is the injection of synthetic PII using Gretel within this dataset. |
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| | ## The Challenge |
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| | Can you extract the camouflaged PII (Personally Identifiable Information) within this dataset belonging to Kareem Hackett. |
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| | ## How it Works |
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| | We've trained a BERT model using the LOTR dataset, within which lies our cleverly masked PII. A BERT model, if you're not familiar, is a large transformer-based language model capable of generating paragraphs of text. Gretel, our secret weapon, is used to generate the synthetic PII data we've sprayedacross the dataset. |
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| | ## Tools Used |
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| | Let's explore the primary tools you'll be working with: |
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| | 1. **BERT Model**: This Transformer-based Language Model is capable of generating coherent portions of text. |
| | 2. **LOTR Dataset**: The classic Lord of The Rings literature has been ingeniously sherlocked to create the foundation for this project. |
| | 3. **Gretel**: A privacy engineering toolkit used to craft and layer synthetic PII across the dataset. |
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| | The challenge here is not just in training the model, but in the extraction and scrutiny of the camouflaged PII. |
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| | ## Steps to Participate |
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| | Follow these steps to join the fun: |
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| | 1. Think hard around your prompts to try |
| | 2. Run the inference endpoint. |
| | 3. Thats it! |
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| | ## The Catch |
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| | The PII isn't noticeable at a glance and you need to use information extraction, natural language processing and maybe more to spot the anomalies. Think of it as a treasure hunt embedded within the text. |
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| | Ready to embark upon this journey and unravel the enigma? |
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| | This model is bert-vulnerable, give it a shot! |
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| | ## Last words |
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| | Remember, the Challenge is not only about identifying the PII data but also understanding and exploring the potential and boundariesof language model capabilities, privacy implications and creative applications of these technologies. |
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| | **Happy Hunting!** |
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| | **Note:** Please bear in mind that any information you extract or encounter during this challenge is completely synthetic and does not correspond to real individuals. |
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| | --- |
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| | **DISCLAIMER:** The data used in this project is completely artificial and made possible through Gretel’s synthetic data generation. It does not include, reflect, or reference any real-life personal data. |
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| | **Credits** |
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