MikeDoes (Michael Anthony)

reacted to their post with 🚀 about 6 hours ago

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626

Can you teach a giant like Google's Gemini to protect user privacy? A new step-by-step guide shows that the answer is a resounding "yes."

While powerful, large language models aren't specialized for privacy tasks. This tutorial by Analytics Vidhya walks through how to fine-tune Gemini into a dedicated tool for PII anonymization.

To teach the model this critical skill, the author needed a robust dataset with thousands of clear 'before' and 'after' examples.

We're thrilled they chose the Ai4Privacy pii-masking-200k dataset for this task. Our data provided the high-quality, paired examples of masked and unmasked text necessary to effectively train Gemini to identify and hide sensitive information accurately.

This is a perfect example of how the community can use open-source data to add a crucial layer of safety to the world's most powerful models. Great work!

🔗 Check out the full tutorial here: https://www.analyticsvidhya.com/blog/2024/03/guide-to-fine-tuning-gemini-for-masking-pii-data/

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

#DataPrivacy #AI #LLM #FineTuning #Anonymization #GoogleGemini #Ai4Privacy #World's largest open privacy masking dataset

posted an update about 7 hours ago

Post

626

Can you teach a giant like Google's Gemini to protect user privacy? A new step-by-step guide shows that the answer is a resounding "yes."

While powerful, large language models aren't specialized for privacy tasks. This tutorial by Analytics Vidhya walks through how to fine-tune Gemini into a dedicated tool for PII anonymization.

To teach the model this critical skill, the author needed a robust dataset with thousands of clear 'before' and 'after' examples.

We're thrilled they chose the Ai4Privacy pii-masking-200k dataset for this task. Our data provided the high-quality, paired examples of masked and unmasked text necessary to effectively train Gemini to identify and hide sensitive information accurately.

This is a perfect example of how the community can use open-source data to add a crucial layer of safety to the world's most powerful models. Great work!

🔗 Check out the full tutorial here: https://www.analyticsvidhya.com/blog/2024/03/guide-to-fine-tuning-gemini-for-masking-pii-data/

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

#DataPrivacy #AI #LLM #FineTuning #Anonymization #GoogleGemini #Ai4Privacy #World's largest open privacy masking dataset

reacted to their post with 👀 about 18 hours ago

Post

4215

What if an AI agent could be tricked into stealing your data, just by reading a tool's description? A new paper reports it's possible.

The "Attractive Metadata Attack" paper details this stealthy new threat. To measure the real-world impact of their attack, the researchers needed a source of sensitive data for the agent to leak. We're proud that the AI4Privacy corpus was used to create the synthetic user profiles containing standardized PII for their experiments.

This is a perfect win-win. Our open-source data helped researchers Kanghua Mo, 龙昱丞, Zhihao Li from Guangzhou University and The Hong Kong Polytechnic University to not just demonstrate a new attack, but also quantify its potential for harm. This data-driven evidence is what pushes the community to build better, execution-level defenses for AI agents.

🔗 Check out their paper to see how easily an agent's trust in tool metadata could be exploited: https://arxiv.org/pdf/2508.02110

#OpenSource
#DataPrivacy
#LLM
#Anonymization
#AIsecurity
#HuggingFace
#Ai4Privacy
#Worldslargestopensourceprivacymaskingdataset

reacted to their post with 🔥 about 18 hours ago

Post

3568

You don't need a massive research lab to build a privacy-preserving AI tool thanks to open datasets. With the right ingredients, anyone can.

A fantastic new guide shows how the democratization of AI is helping to advance safety. It walks through how to use Google's new fine-tuning API to turn Gemini into a powerful tool for PII anonymization.

This project was powered by two key components:

An accessible platform from Google.

High-quality, open-source training data.

We are honored that the author chose the Ai4Privacy pii-masking-200k dataset to provide the crucial data foundation. Our dataset delivered the volume and structure needed to successfully teach a state-of-the-art model how to perform a critical privacy function.

This is the future we're working towards: powerful platforms combined with open, safety-focused data to create tools that benefit everyone. Kudos to the author for showcasing what's possible!

🔗 Read the full step-by-step guide: https://www.analyticsvidhya.com/blog/2024/03/guide-to-fine-tuning-gemini-for-masking-pii-data/

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

#AIforGood #DemocratizeAI #DataPrivacy #Anonymization #OpenSource #LLM #Ai4Privacy

2 replies

·

replied to their post about 18 hours ago

Thank you so much, we appreciate the feedback

reacted to their post with 🚀 2 days ago

Post

3568

You don't need a massive research lab to build a privacy-preserving AI tool thanks to open datasets. With the right ingredients, anyone can.

A fantastic new guide shows how the democratization of AI is helping to advance safety. It walks through how to use Google's new fine-tuning API to turn Gemini into a powerful tool for PII anonymization.

This project was powered by two key components:

An accessible platform from Google.

High-quality, open-source training data.

We are honored that the author chose the Ai4Privacy pii-masking-200k dataset to provide the crucial data foundation. Our dataset delivered the volume and structure needed to successfully teach a state-of-the-art model how to perform a critical privacy function.

This is the future we're working towards: powerful platforms combined with open, safety-focused data to create tools that benefit everyone. Kudos to the author for showcasing what's possible!

🔗 Read the full step-by-step guide: https://www.analyticsvidhya.com/blog/2024/03/guide-to-fine-tuning-gemini-for-masking-pii-data/

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

#AIforGood #DemocratizeAI #DataPrivacy #Anonymization #OpenSource #LLM #Ai4Privacy

2 replies

·

posted an update 2 days ago

Post

3568

You don't need a massive research lab to build a privacy-preserving AI tool thanks to open datasets. With the right ingredients, anyone can.

A fantastic new guide shows how the democratization of AI is helping to advance safety. It walks through how to use Google's new fine-tuning API to turn Gemini into a powerful tool for PII anonymization.

This project was powered by two key components:

An accessible platform from Google.

High-quality, open-source training data.

We are honored that the author chose the Ai4Privacy pii-masking-200k dataset to provide the crucial data foundation. Our dataset delivered the volume and structure needed to successfully teach a state-of-the-art model how to perform a critical privacy function.

This is the future we're working towards: powerful platforms combined with open, safety-focused data to create tools that benefit everyone. Kudos to the author for showcasing what's possible!

🔗 Read the full step-by-step guide: https://www.analyticsvidhya.com/blog/2024/03/guide-to-fine-tuning-gemini-for-masking-pii-data/

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

#AIforGood #DemocratizeAI #DataPrivacy #Anonymization #OpenSource #LLM #Ai4Privacy

2 replies

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reacted to their post with 🚀 2 days ago

Post

1113

Are you sure the open-source model you just downloaded is safe?

A recent paper on "Privacy Backdoors" reports a new vulnerability where pre-trained models can be poisoned before fine-tuning them. This is a serious challenge for everyone building on open-source AI.

Instead of just pointing out problems, we believe in finding better solutions. To understand this threat, the researchers needed to test their attack on realistic data structures. They needed a dataset that could effectively simulate a high-stakes privacy attack, and we're proud that our Ai4Privacy dataset was used to provide this crucial benchmark. The paper reports that for our complex dataset, the privacy leakage on a non-poisoned model was almost zero. After the backdoor attack, that number reportedly jumped to 87%.

Ai4Privacy dataset provided a realistic benchmark for their research. Our dataset, composed of synthetic identities, helped them demonstrate how a poisoned model could dramatically amplify privacy leakage.

This is why we champion open source: it enables the community to identify these issues and develop better, safer solutions together.
Kudos to the authors Yuxin Wen, Leo Marchyok, Sanghyun Hong, Jonas Geiping, Tom Goldstein, and Nicholas Carlini, University of Maryland and Google DeepMind.

🔗 Read the research to understand this new challenge: https://arxiv.org/pdf/2404.01231

🚀 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 🔥 6 days ago

Post

1113

Are you sure the open-source model you just downloaded is safe?

A recent paper on "Privacy Backdoors" reports a new vulnerability where pre-trained models can be poisoned before fine-tuning them. This is a serious challenge for everyone building on open-source AI.

Instead of just pointing out problems, we believe in finding better solutions. To understand this threat, the researchers needed to test their attack on realistic data structures. They needed a dataset that could effectively simulate a high-stakes privacy attack, and we're proud that our Ai4Privacy dataset was used to provide this crucial benchmark. The paper reports that for our complex dataset, the privacy leakage on a non-poisoned model was almost zero. After the backdoor attack, that number reportedly jumped to 87%.

Ai4Privacy dataset provided a realistic benchmark for their research. Our dataset, composed of synthetic identities, helped them demonstrate how a poisoned model could dramatically amplify privacy leakage.

This is why we champion open source: it enables the community to identify these issues and develop better, safer solutions together.
Kudos to the authors Yuxin Wen, Leo Marchyok, Sanghyun Hong, Jonas Geiping, Tom Goldstein, and Nicholas Carlini, University of Maryland and Google DeepMind.

🔗 Read the research to understand this new challenge: https://arxiv.org/pdf/2404.01231

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

posted an update 7 days ago

Post

1113

Are you sure the open-source model you just downloaded is safe?

A recent paper on "Privacy Backdoors" reports a new vulnerability where pre-trained models can be poisoned before fine-tuning them. This is a serious challenge for everyone building on open-source AI.

Instead of just pointing out problems, we believe in finding better solutions. To understand this threat, the researchers needed to test their attack on realistic data structures. They needed a dataset that could effectively simulate a high-stakes privacy attack, and we're proud that our Ai4Privacy dataset was used to provide this crucial benchmark. The paper reports that for our complex dataset, the privacy leakage on a non-poisoned model was almost zero. After the backdoor attack, that number reportedly jumped to 87%.

Ai4Privacy dataset provided a realistic benchmark for their research. Our dataset, composed of synthetic identities, helped them demonstrate how a poisoned model could dramatically amplify privacy leakage.

This is why we champion open source: it enables the community to identify these issues and develop better, safer solutions together.
Kudos to the authors Yuxin Wen, Leo Marchyok, Sanghyun Hong, Jonas Geiping, Tom Goldstein, and Nicholas Carlini, University of Maryland and Google DeepMind.

🔗 Read the research to understand this new challenge: https://arxiv.org/pdf/2404.01231

🚀 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 👀🧠 8 days ago

Post

3733

A single lock on a door isn't enough. Real security is about layers.

The same is true for AI privacy. A new paper, "Whispered Tuning", offers a fantastic layered solution that aims to fortify LLMs against privacy infringements.

We're proud that the first, essential layer, a high-precision PII redaction model was built on the foundation of the Ai4Privacy/pii-65k dataset.

Our dataset provided the necessary training material for their initial anonymization step, which then enabled them to develop further innovations like differential privacy fine-tuning and output filtering. This is a win-win: our data helps create a solid base, and researchers build powerful, multi-stage privacy architectures on top of it.

Together, we're making AI safer.

🔗 Read the full paper to see how a strong foundation enables a complete privacy solution: https://www.scirp.org/journal/paperinformation?paperid=130659

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

posted an update 9 days ago

Post

3733

A single lock on a door isn't enough. Real security is about layers.

The same is true for AI privacy. A new paper, "Whispered Tuning", offers a fantastic layered solution that aims to fortify LLMs against privacy infringements.

We're proud that the first, essential layer, a high-precision PII redaction model was built on the foundation of the Ai4Privacy/pii-65k dataset.

Our dataset provided the necessary training material for their initial anonymization step, which then enabled them to develop further innovations like differential privacy fine-tuning and output filtering. This is a win-win: our data helps create a solid base, and researchers build powerful, multi-stage privacy architectures on top of it.

Together, we're making AI safer.

🔗 Read the full paper to see how a strong foundation enables a complete privacy solution: https://www.scirp.org/journal/paperinformation?paperid=130659

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

posted an update 14 days ago

Post

134

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 16 days ago

Post

1761

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/

1 reply

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reacted to their post with 🔥🚀 19 days ago

Post

3022

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

reacted to their post with 👀 21 days ago

Post

235

The future of AI privacy isn't just in the cloud; it's on your device. But how do we build and validate these tools?

A new paper on "Rescriber" explores this with a tool that uses smaller LLMs for on-device anonymization. Building and validating such tools requires a strong data foundation. We're excited to see that the researchers used the Ai4Privacy open dataset to create their performance benchmarks.

This is our mission in action: providing the open-source data that helps innovators build and test better solutions that will give users more control over their privacy. It's a win for the community when our data helps prove the feasibility of on-device AI for data minimization, with reported user perceptions on par with state-of-the-art cloud models.

Shoutout to Jijie Zhou, Eryue Xu, Yaoyao Wu, and Tianshi Li on this one!

🔗 Check out the research to see how on-device AI, powered by solid data, is changing the game: https://dl.acm.org/doi/pdf/10.1145/3706598.3713701

🚀 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

reacted to their post with 🚀 22 days ago

Post

1031

Why choose between performance, privacy, and transparency when you can have all three?

We're highlighting a solution-oriented paper that introduces PRvL, an open-source toolkit for PII redaction. The interesting part, the researchers used the AI4Privacy-300K and AI4Privacy-500K datasets to train and benchmark their suite of models.

This is the power of open-source collaboration. We provide the comprehensive data foundation, and the community builds better solutions on top of it. It's a win for every organization when this research results in a powerful, free, and self-hostable tool that helps keep their data safe.

Big cheers to Leon Garza, Anantaa Kotal, Aritran Piplai, Lavanya Elluri, Prajit D., and Aman Chadha for pulling this off.

🔗 Read the full paper to see their data-driven results and access the PRvL toolkit: https://arxiv.org/pdf/2508.05545

🚀 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

posted an update 22 days ago

Post

1031

Why choose between performance, privacy, and transparency when you can have all three?

We're highlighting a solution-oriented paper that introduces PRvL, an open-source toolkit for PII redaction. The interesting part, the researchers used the AI4Privacy-300K and AI4Privacy-500K datasets to train and benchmark their suite of models.

This is the power of open-source collaboration. We provide the comprehensive data foundation, and the community builds better solutions on top of it. It's a win for every organization when this research results in a powerful, free, and self-hostable tool that helps keep their data safe.

Big cheers to Leon Garza, Anantaa Kotal, Aritran Piplai, Lavanya Elluri, Prajit D., and Aman Chadha for pulling this off.

🔗 Read the full paper to see their data-driven results and access the PRvL toolkit: https://arxiv.org/pdf/2508.05545

🚀 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

Michael Anthony PRO

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Michael Anthony PRO

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