utter-project/EuroLLM-9B-Instruct
Text Generation • 9B • Updated • 29.2k • 212
Jelle Wassenaar
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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
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