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MikeDoes 
posted an update 3 days ago
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1870
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
MikeDoes 
posted an update 4 days ago
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1259
The tools we use to audit AI for privacy might be easier to fool than we think.

We're highlighting a critical paper that introduces "PoisonM," a novel attack that could make Membership Inference tests unreliable. The direct connection to our work is explicit: the researchers, Neal M., Atul Prakash, Amrita Roy Chowdhury, Ashish Hooda, Kassem Fawaz, Somesh Jha, Zhuohang Li, and Brad Malin used the AI4Privacy dataset as the "canary" dataset in their experiments to test the effectiveness of their attack on realistic, sensitive information.

This is the power of a healthy open-source ecosystem. We provide the foundational data that helps researchers pressure-test our collective assumptions about AI safety. It's a win for everyone when this leads to a more honest conversation about what our tools can and can't do, pushing us all to create better solutions.

🔗 Read the full paper to understand the fundamental flaws in current MI testing: https://arxiv.org/pdf/2506.06003

#OpenSource
#DataPrivacy
#LLM
#Anonymization
#AIsecurity
#HuggingFace
#Ai4Privacy
#Worldslargestopensourceprivacymaskingdataset
MikeDoes 
posted an update 7 days ago
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4150
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
MikeDoes 
posted an update 11 days ago
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1805
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
MikeDoes 
posted an update 18 days ago
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3137
Making LLMs fast with KV-cache sharing is great. A new paper reports it's also a huge privacy risk.

That's why we're excited to see the "SafeKV" paper from researchers at the University of Connecticut, Peking University, and others. Their solution-oriented framework selectively shares non-sensitive data while isolating PII. To validate the "Safe" part of their system, they needed a robust, multilingual privacy benchmark.

We're proud that the Ai4Privacy pii-masking dataset was used for this critical evaluation related to privacy.

This is a perfect win-win. Our open-source data enables researchers to build and validate more effective security solutions for core AI infrastructure. Their work, in turn, helps make the entire LLM ecosystem safer, showing that performance and privacy don't have to be mutually exclusive.

Kudos to Kexin Chu, Zecheng Lin, Dawei Xiang, 沈子旭, Jianchang Su, cheng chu, Yiwei Yang, Wenhui Zhang, Wenfei Wu, and Wei Zhang on this beautiful work.

🔗 Check out their paper to see the future of secure, high-performance LLM inference: https://arxiv.org/pdf/2508.08438

#OpenSource
#DataPrivacy
#LLM
#Anonymization
#AIsecurity
#HuggingFace
#Ai4Privacy
#Worldslargestopensourceprivacymaskingdataset
jstuker 
updated a Space 4 months ago
MikeDoes 
posted an update 5 months ago
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321
Are you sure the open-source LLM 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 research team behind this study: Yuxin Wen, Leo Marchyok, Sanghyun Hong, Jonas Geiping, Tom Goldstein, and Nicholas Carlini, Oregon State University, University of Maryland, Google DeepMind, and ELLIS Institute Tubingen & MPI Intelligent Systems.

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

#DataPrivacy #AI #OpenSource #Anonymization #MachineLearning #Ai4Privacy #Worldslargestopensourceprivacydataset
kechrisc 
authored 4 papers 5 months ago
MikeDoes 
posted an update 5 months ago
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318
When anonymizing data for LLMs, is replacing a name with XXXXX enough?

A great post by Franklin Cardenoso Fernandez argues that we can do better. While simple masking hides data, it often destroys the context that models need to perform well.

A more robust method is contextual anonymization, where PII is replaced with meaningful labels like [NAME] or [ADDRESS]. This protects privacy while preserving the data's structural integrity.

We were pleased to see our Ai4Privacy pii-masking-200k dataset featured in the article as a prime example of this best practice. Our dataset is designed to help developers implement this superior form of anonymization by providing tens of thousands of clear, labeled examples.

By enabling models to be trained on data that is both private and context-rich, we can build AI that is both smarter and safer. This is a core part of our mission.

What's your team's preferred method for data anonymization? Let's discuss best practices.

🔗 Read Franklin's full analysis here: https://www.holisticai.com/blog/managing-personal-data-in-large-language-models

#DataPrivacy #Anonymization #ResponsibleAI #LLM #MachineLearning #AIEthics #Ai4Privacy #World's largest open privacy masking dataset
JackDapid 
updated a Space 5 months ago
JackDapid 
published a Space 5 months ago
MikeDoes 
posted an update 5 months ago
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2030
🛡️ At Ai4Privacy, our goal is to empower researchers to build a safer AI ecosystem. Today, we're highlighting crucial research that does just that by exposing a new vulnerability.

The paper "Forget to Flourish" details a new model poisoning technique. It's a reminder that as we fine-tune LLMs, our anonymization and privacy strategies must evolve to counter increasingly sophisticated threats.

We're proud that the Ai4Privacy dataset was instrumental in this study. It served two key purposes:

Provided a Realistic Testbed: It gave the researchers access to a diverse set of synthetic and realistic PII samples in a safe, controlled environment.

Enabled Impactful Benchmarking: It allowed them to measure the actual effectiveness of their data extraction attack, proving it could compromise specific, high-value information.

This work reinforces our belief that progress in AI security is a community effort. By providing robust tools for benchmarking, we can collectively identify weaknesses and build stronger, more resilient systems. A huge congratulations to the authors on this important contribution.

🔗 Read the full paper: https://arxiv.org/html/2408.17354v1

#OpenSource #DataPrivacy #LLM #Anonymization #AIsecurity #HuggingFace #Ai4Privacy #World's largest open privacy masking dataset
MikeDoes 
posted an update 6 months ago
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1153
In data privacy, 92% accuracy is not an A-grade. Privacy AI needs to be better.

That's the stark takeaway from a recent benchmark by Diego Mouriño

(Making Science), who put today's top PII detection methods to the test on call center transcripts using the Ai4Privacy dataset.

They pitted cutting-edge LLMs (like GPT-4 & Gemini) against traditional systems (like Cloud DLPs). The results show that our trust in these tools might be misplaced.



📊 The Hard Numbers:



Even top-tier LLMs peaked at a reported 92% accuracy, leaving a potential dangerous 8% gap where your customer's data can leak. They particularly struggled with basics like 'last names' and 'street addresses'.



The old guard? Traditional rule-based systems reportedly achieved a shocking 50% accuracy. A coin toss with your customers' privacy.


This tells us that for privacy tasks, off-the-shelf accuracy is a vanity metric. The real metric is the cost of a single failure—one leaked name, one exposed address.



While no tool is perfect, some are better than others. Diego’s full analysis breaks down which models offer the best cost-to-accuracy balance in this flawed landscape. It's a must-read for anyone serious about building trustworthy AI.

#DataPrivacy #AI #LLM #RiskManagement #MetricsThatMatter #InfoSec

Find the full post here:
https://www.makingscience.com/blog/protecting-customer-privacy-how-to-remove-pii-from-call-center-transcripts/

Dataset:
ai4privacy/pii-masking-400k
MikeDoes 
posted an update 7 months ago
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2726
Started
aistatuscodes as a new project to create codes to understand AI performance better.

Going to be posting daily here and on instagram until we get to 100m downloads :)
https://www.instagram.com/MikeDoesDo/

Follow along the journey!
MikeDoes 
posted an update 8 months ago
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1558
PII-Masking-1M Final Day (7/7)! 🚀 Today, we unveil 5 NEW Enterprise PII (E-PII) Dataset PREVIEWS!

Standard PII tools often miss sensitive *business* data. That's why we built E-PII previews for the data that powers your operations and compliance needs.

Get a first look (representing 100,000 samples each!) into datasets designed for real-world enterprise security across these categories:

🏥 **PHI Preview**: For Healthcare Data
💳 **PFI Preview:** For Financial Data
🏢 **PWI Preview:** For Workplace Data
💻 **PDI Preview:** For Digital Activity Data
📍 **PLI Preview:** For Location Data


That wraps up our #PIIMasking1M 7 days announcement! HUGE thanks for following along and for your engagement.
Explore ALL our releases, including these E-PII previews, in the Ai4Privacy Hugging Face Collection & show some love ❤️ if you find them useful!
🔗 Visit the Collection:https://huggingface.co/ai4privacy

Let's keep building safer AI, together!
MikeDoes 
posted an update 9 months ago
MikeDoes 
posted an update 9 months ago
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2800
🚀 We are quite excited to announce the Ai4Privacy Python library! 🎉

pip install ai4privacy to anonymize short english text with OpenPII Masking 500k labels

📊 Day 5/7 of PII Masking 1M announcements complete! ⏰