openai/privacy-filter
Token Classification • 1B • Updated • 141k • 1.29k
Michael Anthony PRO
MikeDoes
AI & ML interests
Privacy, Large Language Model, Explainable
Recent Activity
liked a model about 13 hours ago
openai/privacy-filter reacted to theirpost with 🔥 about 21 hours ago
AI4Privacy datasets are being used to decide what data should never leave the device.
A new paper on privacy-preserving cloud computing uses the AI4Privacy PII-Masking-65K dataset to train models that classify text as private or public before it’s ever sent to the cloud.
This is a subtle but important shift.
Instead of encrypting everything or trusting the cloud by default, the authors ask a simpler question:
Can we detect sensitive text early enough to keep it local?
Using DistilBERT, trained partly on AI4Privacy PII data, the system learns to:
route private text to local processing
send non-sensitive text to the cloud
train collaboratively using federated learning, without sharing raw data
The result:
99.9% accuracy in private vs public text detection
Near-centralized performance in downstream tasks like SMS spam detection
Privacy protection enforced by design, not policy
What stands out here is not just the model performance, but the architectural idea:
privacy as a routing decision, backed by large-scale PII annotations.
This work reinforces a pattern we keep seeing: scalable privacy systems don’t start with encryption, they start with good PII data.
📄 Full Paper here: https://dl.acm.org/doi/full/10.1145/3773276.3774872
#Ai4Privacy #DataPrivacy #PIIMasking #FederatedLearning #PrivacyEngineering #OpenSourceAI #ResponsibleAI #AcademicResearch #LLMSecurity posted an update 1 day ago
AI4Privacy datasets are being used to decide what data should never leave the device.
A new paper on privacy-preserving cloud computing uses the AI4Privacy PII-Masking-65K dataset to train models that classify text as private or public before it’s ever sent to the cloud.
This is a subtle but important shift.
Instead of encrypting everything or trusting the cloud by default, the authors ask a simpler question:
Can we detect sensitive text early enough to keep it local?
Using DistilBERT, trained partly on AI4Privacy PII data, the system learns to:
route private text to local processing
send non-sensitive text to the cloud
train collaboratively using federated learning, without sharing raw data
The result:
99.9% accuracy in private vs public text detection
Near-centralized performance in downstream tasks like SMS spam detection
Privacy protection enforced by design, not policy
What stands out here is not just the model performance, but the architectural idea:
privacy as a routing decision, backed by large-scale PII annotations.
This work reinforces a pattern we keep seeing: scalable privacy systems don’t start with encryption, they start with good PII data.
📄 Full Paper here: https://dl.acm.org/doi/full/10.1145/3773276.3774872
#Ai4Privacy #DataPrivacy #PIIMasking #FederatedLearning #PrivacyEngineering #OpenSourceAI #ResponsibleAI #AcademicResearch #LLMSecurityOrganizations
liked a
model about 13 hours ago
reacted to their post with 🔥 about 21 hours ago
Post
131
AI4Privacy datasets are being used to decide what data should never leave the device.
A new paper on privacy-preserving cloud computing uses the AI4Privacy PII-Masking-65K dataset to train models that classify text as private or public before it’s ever sent to the cloud.
This is a subtle but important shift.
Instead of encrypting everything or trusting the cloud by default, the authors ask a simpler question:
Can we detect sensitive text early enough to keep it local?
Using DistilBERT, trained partly on AI4Privacy PII data, the system learns to:
route private text to local processing
send non-sensitive text to the cloud
train collaboratively using federated learning, without sharing raw data
The result:
99.9% accuracy in private vs public text detection
Near-centralized performance in downstream tasks like SMS spam detection
Privacy protection enforced by design, not policy
What stands out here is not just the model performance, but the architectural idea:
privacy as a routing decision, backed by large-scale PII annotations.
This work reinforces a pattern we keep seeing: scalable privacy systems don’t start with encryption, they start with good PII data.
📄 Full Paper here: https://dl.acm.org/doi/full/10.1145/3773276.3774872
#Ai4Privacy #DataPrivacy #PIIMasking #FederatedLearning #PrivacyEngineering #OpenSourceAI #ResponsibleAI #AcademicResearch #LLMSecurity
A new paper on privacy-preserving cloud computing uses the AI4Privacy PII-Masking-65K dataset to train models that classify text as private or public before it’s ever sent to the cloud.
This is a subtle but important shift.
Instead of encrypting everything or trusting the cloud by default, the authors ask a simpler question:
Can we detect sensitive text early enough to keep it local?
Using DistilBERT, trained partly on AI4Privacy PII data, the system learns to:
route private text to local processing
send non-sensitive text to the cloud
train collaboratively using federated learning, without sharing raw data
The result:
99.9% accuracy in private vs public text detection
Near-centralized performance in downstream tasks like SMS spam detection
Privacy protection enforced by design, not policy
What stands out here is not just the model performance, but the architectural idea:
privacy as a routing decision, backed by large-scale PII annotations.
This work reinforces a pattern we keep seeing: scalable privacy systems don’t start with encryption, they start with good PII data.
📄 Full Paper here: https://dl.acm.org/doi/full/10.1145/3773276.3774872
#Ai4Privacy #DataPrivacy #PIIMasking #FederatedLearning #PrivacyEngineering #OpenSourceAI #ResponsibleAI #AcademicResearch #LLMSecurity
posted an update 1 day ago
Post
131
AI4Privacy datasets are being used to decide what data should never leave the device.
A new paper on privacy-preserving cloud computing uses the AI4Privacy PII-Masking-65K dataset to train models that classify text as private or public before it’s ever sent to the cloud.
This is a subtle but important shift.
Instead of encrypting everything or trusting the cloud by default, the authors ask a simpler question:
Can we detect sensitive text early enough to keep it local?
Using DistilBERT, trained partly on AI4Privacy PII data, the system learns to:
route private text to local processing
send non-sensitive text to the cloud
train collaboratively using federated learning, without sharing raw data
The result:
99.9% accuracy in private vs public text detection
Near-centralized performance in downstream tasks like SMS spam detection
Privacy protection enforced by design, not policy
What stands out here is not just the model performance, but the architectural idea:
privacy as a routing decision, backed by large-scale PII annotations.
This work reinforces a pattern we keep seeing: scalable privacy systems don’t start with encryption, they start with good PII data.
📄 Full Paper here: https://dl.acm.org/doi/full/10.1145/3773276.3774872
#Ai4Privacy #DataPrivacy #PIIMasking #FederatedLearning #PrivacyEngineering #OpenSourceAI #ResponsibleAI #AcademicResearch #LLMSecurity
A new paper on privacy-preserving cloud computing uses the AI4Privacy PII-Masking-65K dataset to train models that classify text as private or public before it’s ever sent to the cloud.
This is a subtle but important shift.
Instead of encrypting everything or trusting the cloud by default, the authors ask a simpler question:
Can we detect sensitive text early enough to keep it local?
Using DistilBERT, trained partly on AI4Privacy PII data, the system learns to:
route private text to local processing
send non-sensitive text to the cloud
train collaboratively using federated learning, without sharing raw data
The result:
99.9% accuracy in private vs public text detection
Near-centralized performance in downstream tasks like SMS spam detection
Privacy protection enforced by design, not policy
What stands out here is not just the model performance, but the architectural idea:
privacy as a routing decision, backed by large-scale PII annotations.
This work reinforces a pattern we keep seeing: scalable privacy systems don’t start with encryption, they start with good PII data.
📄 Full Paper here: https://dl.acm.org/doi/full/10.1145/3773276.3774872
#Ai4Privacy #DataPrivacy #PIIMasking #FederatedLearning #PrivacyEngineering #OpenSourceAI #ResponsibleAI #AcademicResearch #LLMSecurity
reacted to their post with ❤️ 2 days ago
Post
137
This new preprint fine-tunes T5-small and Mistral-7B on the AI4Privacy PII-Masking-200K dataset and shows that lightweight models can match and sometimes rival much larger LLMs for privacy tasks.
The study tackles a real deployment question many teams face:
Is PII masking a model-size problem, or a data-quality problem?
Using AI4Privacy’s large-scale, standardized PII annotations, the authors systematically compare:
Encoder–decoder models (T5) vs
Decoder-only models (Mistral)
across accuracy, robustness, latency, and real-world conversational text.
What stood out:
Mistral-7B achieved higher recall and robustness across noisy, informal inputs but with 10× higher latency
T5-small, trained on the same AI4Privacy data, delivered fast, structured, low-cost masking, making it viable for real-time systems
Dataset normalization (not model size) was one of the biggest drivers of performance gains
The models were then deployed in a live Discord bot, where performance dropped under real-world conditions a reminder that benchmarks alone aren’t enough.
The takeaway is hard to ignore:
Privacy-preserving AI scales through data design, not just bigger models.
This work reinforces why open, well-curated datasets like AI4Privacy PII-Masking-200K are becoming foundational infrastructure for privacy-first AI especially for teams that need self-hosted, transparent solutions.
📄 Read the paper: https://arxiv.org/abs/2512.18608
The study tackles a real deployment question many teams face:
Is PII masking a model-size problem, or a data-quality problem?
Using AI4Privacy’s large-scale, standardized PII annotations, the authors systematically compare:
Encoder–decoder models (T5) vs
Decoder-only models (Mistral)
across accuracy, robustness, latency, and real-world conversational text.
What stood out:
Mistral-7B achieved higher recall and robustness across noisy, informal inputs but with 10× higher latency
T5-small, trained on the same AI4Privacy data, delivered fast, structured, low-cost masking, making it viable for real-time systems
Dataset normalization (not model size) was one of the biggest drivers of performance gains
The models were then deployed in a live Discord bot, where performance dropped under real-world conditions a reminder that benchmarks alone aren’t enough.
The takeaway is hard to ignore:
Privacy-preserving AI scales through data design, not just bigger models.
This work reinforces why open, well-curated datasets like AI4Privacy PII-Masking-200K are becoming foundational infrastructure for privacy-first AI especially for teams that need self-hosted, transparent solutions.
📄 Read the paper: https://arxiv.org/abs/2512.18608
posted an update 2 days ago
Post
137
This new preprint fine-tunes T5-small and Mistral-7B on the AI4Privacy PII-Masking-200K dataset and shows that lightweight models can match and sometimes rival much larger LLMs for privacy tasks.
The study tackles a real deployment question many teams face:
Is PII masking a model-size problem, or a data-quality problem?
Using AI4Privacy’s large-scale, standardized PII annotations, the authors systematically compare:
Encoder–decoder models (T5) vs
Decoder-only models (Mistral)
across accuracy, robustness, latency, and real-world conversational text.
What stood out:
Mistral-7B achieved higher recall and robustness across noisy, informal inputs but with 10× higher latency
T5-small, trained on the same AI4Privacy data, delivered fast, structured, low-cost masking, making it viable for real-time systems
Dataset normalization (not model size) was one of the biggest drivers of performance gains
The models were then deployed in a live Discord bot, where performance dropped under real-world conditions a reminder that benchmarks alone aren’t enough.
The takeaway is hard to ignore:
Privacy-preserving AI scales through data design, not just bigger models.
This work reinforces why open, well-curated datasets like AI4Privacy PII-Masking-200K are becoming foundational infrastructure for privacy-first AI especially for teams that need self-hosted, transparent solutions.
📄 Read the paper: https://arxiv.org/abs/2512.18608
The study tackles a real deployment question many teams face:
Is PII masking a model-size problem, or a data-quality problem?
Using AI4Privacy’s large-scale, standardized PII annotations, the authors systematically compare:
Encoder–decoder models (T5) vs
Decoder-only models (Mistral)
across accuracy, robustness, latency, and real-world conversational text.
What stood out:
Mistral-7B achieved higher recall and robustness across noisy, informal inputs but with 10× higher latency
T5-small, trained on the same AI4Privacy data, delivered fast, structured, low-cost masking, making it viable for real-time systems
Dataset normalization (not model size) was one of the biggest drivers of performance gains
The models were then deployed in a live Discord bot, where performance dropped under real-world conditions a reminder that benchmarks alone aren’t enough.
The takeaway is hard to ignore:
Privacy-preserving AI scales through data design, not just bigger models.
This work reinforces why open, well-curated datasets like AI4Privacy PII-Masking-200K are becoming foundational infrastructure for privacy-first AI especially for teams that need self-hosted, transparent solutions.
📄 Read the paper: https://arxiv.org/abs/2512.18608
posted an update 4 days ago
Post
120
PII leakage isn’t just a model problem it’s a data problem.
A recent paper takes a hard look at how well current systems actually detect and redact personal data at scale. One of their key conclusions is something the privacy community keeps rediscovering: without large, structured, and diverse PII datasets, evaluation collapses into guesswork.
To ground their experiments, the authors benchmarked their approach using the 500K PII-Masking dataset from AI4Privacy, leveraging its scale and coverage to test real-world redaction behavior rather than toy examples.
What’s interesting here isn’t just the model performance it’s what the evaluation reveals.
The paper shows that many systems appear robust under narrow tests but fail once PII appears in varied formats, contexts, and combinations. This gap between “works in theory” and “works in practice” is exactly where privacy risks emerge.
This is the value of open, research-grade datasets:
They expose failure modes early
They make comparisons reproducible
They let the community measure progress honestly
When researchers build on shared data foundations, everyone benefits from academic insight to safer downstream applications.
🔗 Read the full paper here: https://arxiv.org/abs/2407.08792
A recent paper takes a hard look at how well current systems actually detect and redact personal data at scale. One of their key conclusions is something the privacy community keeps rediscovering: without large, structured, and diverse PII datasets, evaluation collapses into guesswork.
To ground their experiments, the authors benchmarked their approach using the 500K PII-Masking dataset from AI4Privacy, leveraging its scale and coverage to test real-world redaction behavior rather than toy examples.
What’s interesting here isn’t just the model performance it’s what the evaluation reveals.
The paper shows that many systems appear robust under narrow tests but fail once PII appears in varied formats, contexts, and combinations. This gap between “works in theory” and “works in practice” is exactly where privacy risks emerge.
This is the value of open, research-grade datasets:
They expose failure modes early
They make comparisons reproducible
They let the community measure progress honestly
When researchers build on shared data foundations, everyone benefits from academic insight to safer downstream applications.
🔗 Read the full paper here: https://arxiv.org/abs/2407.08792
reacted to davidmezzetti's post with ❤️ 6 days ago
Post
149
We're excited to release the new BiomedBERT Small series of models. These 22.7M parameter models, trained for medical literature, are similarity sized to the popular all-MiniLM-v2 models and pack quite a punch.
Read more here: https://huggingface.co/blog/NeuML/biomedbert-small
Read more here: https://huggingface.co/blog/NeuML/biomedbert-small
published 10
datasets 8 days ago
ai4privacy/pii-masking-work-pwi-200k
Viewer • Updated • 252k • 6
ai4privacy/pii-masking-digital-pdi-200k
Viewer • Updated • 198k • 6
ai4privacy/pii-masking-location-pli-200k
Viewer • Updated • 257k • 6
ai4privacy/pii-masking-financial-pfi-200k
Viewer • Updated • 257k • 8
ai4privacy/pwi-masking-100k-full
Viewer • Updated • 91.5k • 8
ai4privacy/pdi-masking-100k-full
Viewer • Updated • 91.4k • 8
ai4privacy/pli-masking-100k-full
Viewer • Updated • 91.3k • 10 • 1
ai4privacy/phi-masking-100k-full
Viewer • Updated • 91.3k • 7
ai4privacy/pfi-masking-100k-full
Viewer • Updated • 160k • 9 • 1
ai4privacy/pii-masking-400k-extended
Viewer • Updated • 407k • 7
liked a
dataset 15 days ago
published a
dataset 15 days ago