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OkAndThisHappened

EtoUbivaetMnya1997

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reacted to MikeDoes's post with ❤️ about 1 month ago

Anonymizing a prompt is half the battle. Reliably de-anonymizing the response is the other. To build a truly reliable privacy pipeline, you have to test it. A new Master's thesis does just that, and our data was there for every step. We're excited to showcase this work on handling confidential data in LLM prompts from Nedim Karavdic at Mälardalen University. To build their PII anonymization pipeline, they first trained a custom NER model. We're proud that the Ai4Privacy pii-masking-200k dataset was used as the foundational training data for this critical first step. But it didn't stop there. The research also used our dataset to create the parallel data needed to train and test the generative "Seek" models for de-anonymization. It's a win-win when our open-source data not only helps build the proposed "better solution" but also helps prove why it's better by enabling a rigorous, data-driven comparison. 🔗 Check out the full thesis for a great deep-dive into building a practical, end-to-end privacy solution: https://www.diva-portal.org/smash/get/diva2:1980696/FULLTEXT01.pdf #OpenSource #DataPrivacy #LLM #Anonymization #AIsecurity #HuggingFace #Ai4Privacy #Worldslargestopensourceprivacymaskingdataset

liked a dataset 2 months ago

FLARE-MedFM/FLARE-Task5-MLLM-2D

liked a dataset 2 months ago

openai/gsm8k

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