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http://www.ai4privacy.com

AI & ML interests

Privacy, Large Language Model, Explainable

Recent Activity

liked a model 1 day ago
openai/privacy-filter
reacted to theirpost with 🔥 2 days 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 2 days 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
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