Michael Anthony PRO

MikeDoes

http://www.ai4privacy.com

AI & ML interests

Privacy, Large Language Model, Explainable

Recent Activity

posted an update about 9 hours ago

How can we teach a robot to understand the nuances of privacy in elderly care? It starts with teaching it to recognize sensitive data. A new conceptual paper introduces "Privacy Agents" an AI designed to safeguard contextual integrity in care settings. To demonstrate that their innovative concept is feasible, the researchers from TU Wien needed to prove an AI could identify PII in a real-world transcript. We're proud that the tool they used for this proof-of-concept was fine-tuned on the Ai4Privacy/pii-masking-200k dataset. This is a perfect win-win: brilliant researchers are designing the future of privacy-aware robotics, and our open-source data helps provide the foundational tools to show it's possible. This is how conceptual breakthroughs become practical solutions. 🔗 Check out their forward-thinking paper on the future of privacy in Human-Robot Interaction: http://hirschmanner.com/publication/privacy-hri-2024/privacy-hri-2024.pdf 🚀 Stay updated on the latest in privacy-preserving AI—follow us on LinkedIn: https://www.linkedin.com/company/ai4privacy/posts/

posted an update 2 days ago

What happens when an LLM "forgets" your data? A new paper reports it might not be gone for good. The "Janus Interface" paper details a new attack that could recover forgotten PII through fine-tuning APIs. This is a solution-oriented paper because it highlights a problem that needs fixing. Testing such a high-stakes attack requires equally high-stakes data. The Ai4Privacy 300k dataset was a key part of their evaluation, providing a testbed for extracting sensitive Social Security Numbers. Our dataset, with its synthetic structured SSN data, helped the researchers at Indiana University, Stanford & CISPA, and others demonstrate that their attack works on more than just emails. It could affect highly sensitive personal identifiers. We're excited to see our open-source dataset used in such cutting-edge security research. It's a win for the community when researchers can use our resources to stress-test the safety of modern AI systems. This work is a direct and explicit call for stronger protections on fine-tuning interfaces. 🔗 This is why open data for security research is so important. Check out the full paper: https://arxiv.org/pdf/2310.15469 🚀 Stay updated on the latest in privacy-preserving AI—follow us on LinkedIn: https://www.linkedin.com/company/ai4privacy/posts/

reacted to their post with 🔥 5 days ago

How do you prove your new, specialized AI model is a better solution? You test it against the best. That's why we were excited to see the new AdminBERT paper from researchers at Nantes Université and others. To show the strength of their new model for French administrative texts, they compared it to the state-of-the-art generalist model, NERmemBERT. The direct connection to our work is clear: NERmemBERT was trained on a combination of datasets, including the Pii-masking-200k dataset by Ai4Privacy. This is a perfect win-win for the open-source community. Our foundational dataset helps create a strong, general-purpose benchmark, which in turn helps researchers prove the value of their specialized work. This is how we all get better. 🔗 Great work by Thomas Sebbag, Solen Quiniou, Nicolas Stucky, and Emmanuel Morin on tackling a challenging domain! Check out their paper: https://aclanthology.org/2025.coling-main.27.pdf 🚀 Stay updated on the latest in privacy-preserving AI—follow us on LinkedIn: https://www.linkedin.com/company/ai4privacy/posts/ #OpenSource #DataPrivacy #LLM #Anonymization #AIsecurity #HuggingFace #Ai4Privacy #Worldslargestopensourceprivacymaskingdataset

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How can we teach a robot to understand the nuances of privacy in elderly care? It starts with teaching it to recognize sensitive data.

A new conceptual paper introduces "Privacy Agents" an AI designed to safeguard contextual integrity in care settings. To demonstrate that their innovative concept is feasible, the researchers from TU Wien needed to prove an AI could identify PII in a real-world transcript.

We're proud that the tool they used for this proof-of-concept was fine-tuned on the Ai4Privacy/pii-masking-200k dataset.

This is a perfect win-win: brilliant researchers are designing the future of privacy-aware robotics, and our open-source data helps provide the foundational tools to show it's possible. This is how conceptual breakthroughs become practical solutions.

🔗 Check out their forward-thinking paper on the future of privacy in Human-Robot Interaction: http://hirschmanner.com/publication/privacy-hri-2024/privacy-hri-2024.pdf

🚀 Stay updated on the latest in privacy-preserving AI—follow us on LinkedIn: https://www.linkedin.com/company/ai4privacy/posts/

Post

1725

What happens when an LLM "forgets" your data? A new paper reports it might not be gone for good.

The "Janus Interface" paper details a new attack that could recover forgotten PII through fine-tuning APIs. This is a solution-oriented paper because it highlights a problem that needs fixing.

Testing such a high-stakes attack requires equally high-stakes data. The Ai4Privacy 300k dataset was a key part of their evaluation, providing a testbed for extracting sensitive Social Security Numbers. Our dataset, with its synthetic structured SSN data, helped the researchers at Indiana University, Stanford & CISPA, and others demonstrate that their attack works on more than just emails. It could affect highly sensitive personal identifiers.

We're excited to see our open-source dataset used in such cutting-edge security research. It's a win for the community when researchers can use our resources to stress-test the safety of modern AI systems. This work is a direct and explicit call for stronger protections on fine-tuning interfaces.

🔗 This is why open data for security research is so important. Check out the full paper: https://arxiv.org/pdf/2310.15469

🚀 Stay updated on the latest in privacy-preserving AI—follow us on LinkedIn: https://www.linkedin.com/company/ai4privacy/posts/

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