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Michael Anthony PRO
MikeDoes
AI & ML interests
Privacy, Large Language Model, Explainable
Recent Activity
posted
an
update
about 4 hours ago
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
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with ❤️
2 days ago
Building powerful multilingual AI shouldn't mean sacrificing user privacy.
We're highlighting a solution-oriented report from researchers Sahana Naganandh, Vaibhav V, and Thenmozhi M at Vellore Institute of Technology that investigates this exact challenge. The direct connection to our mission is clear: the paper showcases the PII43K dataset as a privacy-preserving alternative to high-risk, raw multilingual data
The report notes that our dataset, with its structured anonymization, is a "useful option for privacy-centric AI applications." It's always a delight when academic research independently validates our data-first approach to solving real-world privacy problems.
This is how we build a safer AI future together.
🔗 Read the full report here to learn more: https://assets.cureusjournals.com/artifacts/upload/technical_report/pdf/3689/20250724-59151-93w9ar.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 👀
2 days ago
Building powerful multilingual AI shouldn't mean sacrificing user privacy.
We're highlighting a solution-oriented report from researchers Sahana Naganandh, Vaibhav V, and Thenmozhi M at Vellore Institute of Technology that investigates this exact challenge. The direct connection to our mission is clear: the paper showcases the PII43K dataset as a privacy-preserving alternative to high-risk, raw multilingual data
The report notes that our dataset, with its structured anonymization, is a "useful option for privacy-centric AI applications." It's always a delight when academic research independently validates our data-first approach to solving real-world privacy problems.
This is how we build a safer AI future together.
🔗 Read the full report here to learn more: https://assets.cureusjournals.com/artifacts/upload/technical_report/pdf/3689/20250724-59151-93w9ar.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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