Running
1
Terminal Visualiser
💻
Create and download styled terminal screenshots
Michael Anthony PRO
MikeDoes
AI & ML interests
Privacy, Large Language Model, Explainable
Recent Activity
reacted
to
their
post
with 👀
2 days ago
How do you protect your prompts without breaking them? You need a smart sanitizer. A new system called Prϵϵmpt shows how.
The first, critical step in their solution is a high-performance Named Entity Recognition (NER) model to find the sensitive data. We're proud to see that these researchers, Amrita Roy Chowdhury, David Glukhov, Divyam Anshumaan, Prasad Chalasani, Nicolas Papernot, Somesh Jha, and Mihir Bellare from the University of Michigan, University of Toronto, University of Wisconsin-Madison, University of California, San Diego - Rady School of Management and Langroid Incorporated fine-tuned their NER model on 10 high-risk categories from the AI4Privacy dataset.
This is a perfect win-win. Our open-source data helps provide the foundation for the critical detection engine, which in turn enables the community to build and test better solutions like Prϵϵmpt's innovative use of encryption and Differential Privacy.
🔗 Check out their paper for a deep dive into a formally private, high-utility prompt sanitizer: https://arxiv.org/pdf/2504.05147
#OpenSource
#DataPrivacy
#LLM
#Anonymization
#AIsecurity
#HuggingFace
#Ai4Privacy
#Worldslargestopensourceprivacymaskingdataset
reacted
to
their
post
with 🚀
2 days ago
How do you protect your prompts without breaking them? You need a smart sanitizer. A new system called Prϵϵmpt shows how.
The first, critical step in their solution is a high-performance Named Entity Recognition (NER) model to find the sensitive data. We're proud to see that these researchers, Amrita Roy Chowdhury, David Glukhov, Divyam Anshumaan, Prasad Chalasani, Nicolas Papernot, Somesh Jha, and Mihir Bellare from the University of Michigan, University of Toronto, University of Wisconsin-Madison, University of California, San Diego - Rady School of Management and Langroid Incorporated fine-tuned their NER model on 10 high-risk categories from the AI4Privacy dataset.
This is a perfect win-win. Our open-source data helps provide the foundation for the critical detection engine, which in turn enables the community to build and test better solutions like Prϵϵmpt's innovative use of encryption and Differential Privacy.
🔗 Check out their paper for a deep dive into a formally private, high-utility prompt sanitizer: https://arxiv.org/pdf/2504.05147
#OpenSource
#DataPrivacy
#LLM
#Anonymization
#AIsecurity
#HuggingFace
#Ai4Privacy
#Worldslargestopensourceprivacymaskingdataset
posted
an
update
3 days ago
How do you protect your prompts without breaking them? You need a smart sanitizer. A new system called Prϵϵmpt shows how.
The first, critical step in their solution is a high-performance Named Entity Recognition (NER) model to find the sensitive data. We're proud to see that these researchers, Amrita Roy Chowdhury, David Glukhov, Divyam Anshumaan, Prasad Chalasani, Nicolas Papernot, Somesh Jha, and Mihir Bellare from the University of Michigan, University of Toronto, University of Wisconsin-Madison, University of California, San Diego - Rady School of Management and Langroid Incorporated fine-tuned their NER model on 10 high-risk categories from the AI4Privacy dataset.
This is a perfect win-win. Our open-source data helps provide the foundation for the critical detection engine, which in turn enables the community to build and test better solutions like Prϵϵmpt's innovative use of encryption and Differential Privacy.
🔗 Check out their paper for a deep dive into a formally private, high-utility prompt sanitizer: https://arxiv.org/pdf/2504.05147
#OpenSource
#DataPrivacy
#LLM
#Anonymization
#AIsecurity
#HuggingFace
#Ai4Privacy
#Worldslargestopensourceprivacymaskingdataset
Organizations
