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Michael Anthony PRO
MikeDoes
AI & ML interests
Privacy, Large Language Model, Explainable
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3 days ago
How do you prove a new AI privacy tool actually works? You test it against a world-class benchmark.
That's why we're proud our data played a key role in the research for "Rescriber," a new browser extension for user-led anonymization. To objectively measure their tool's performance against other methods, the researchers needed a diverse and challenging evaluation set.
They built their benchmark using 240 samples from the Ai4Privacy open dataset.
This is a win-win for the ecosystem: our open-source data helps researchers validate their innovative solutions, and in turn, their work pushes the entire field of privacy-preserving AI forward. The "Rescriber" tool is a fantastic step towards on-device, user-controlled privacy.
🔗 Learn more about their data-driven findings in the full paper: https://arxiv.org/pdf/2410.11876
🚀 Stay updated on the latest in privacy-preserving AI—follow us on LinkedIn: https://www.linkedin.com/company/ai4privacy/posts/
#DataPrivacy #AI #OpenSource #Anonymization #MachineLearning #HealthcareAI #Ai4Privacy
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State-of-the-art AI doesn't start with a model. It starts with the data.
Achieving near-perfect accuracy for PII & PHI
anonymization is one of the toughest challenges in NLP. A model is only as good as the data it learns from, providing this foundational layer is central to our mission. The
ai4privacy/pii-masking-400k dataset was built for this exact purpose: to serve as a robust, large-scale, open-source training ground for building high-precision privacy tools.
To see the direct impact of this data-first approach, look at the ner_deid_aipii model for Healthcare NLP by johnsnow lab. By training on our 400,000 labeled examples, the model achieved incredible performance:
100% F1-score on EMAIL detection.
99% F1-score on PHONE detection.
97% F1-score on NAME detection.
This is the result of combining a cutting-edge architecture with a comprehensive, high-quality dataset. We provide the open-source foundation so developers can build better, safer solutions.
Explore the dataset that helps power these next-generation privacy tools: https://huggingface.co/datasets/ai4privacy/pii-masking-400k
🚀 Stay updated on the latest in privacy-preserving AI—follow us on LinkedIn: https://www.linkedin.com/company/ai4privacy/posts/
#DataPrivacy #AI #OpenSource #Anonymization #MachineLearning #HealthcareAI #Ai4Privacy
posted
an
update
3 days ago
How do you prove a new AI privacy tool actually works? You test it against a world-class benchmark.
That's why we're proud our data played a key role in the research for "Rescriber," a new browser extension for user-led anonymization. To objectively measure their tool's performance against other methods, the researchers needed a diverse and challenging evaluation set.
They built their benchmark using 240 samples from the Ai4Privacy open dataset.
This is a win-win for the ecosystem: our open-source data helps researchers validate their innovative solutions, and in turn, their work pushes the entire field of privacy-preserving AI forward. The "Rescriber" tool is a fantastic step towards on-device, user-controlled privacy.
🔗 Learn more about their data-driven findings in the full paper: https://arxiv.org/pdf/2410.11876
🚀 Stay updated on the latest in privacy-preserving AI—follow us on LinkedIn: https://www.linkedin.com/company/ai4privacy/posts/
#DataPrivacy #AI #OpenSource #Anonymization #MachineLearning #HealthcareAI #Ai4Privacy
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