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Found 6 papers, 2 papers with code
Converting Transformers to Polynomial Form for Secure Inference Over Homomorphic Encryption
This innovation enables us to perform secure inference on LMs with WikiText-103.
Training Large Scale Polynomial CNNs for E2E Inference over Homomorphic Encryption
Training large-scale CNNs that during inference can be run under Homomorphic Encryption (HE) is challenging due to the need to use only polynomial operations.
HeLayers: A Tile Tensors Framework for Large Neural Networks on Encrypted Data
We present a simple and intuitive framework that abstracts the packing decision for the user.
Data Minimization for GDPR Compliance in Machine Learning Models
The EU General Data Protection Regulation (GDPR) mandates the principle of data minimization, which requires that only data necessary to fulfill a certain purpose be collected.
Anonymizing Machine Learning Models
Anonymized data, however, is exempt from the obligations set out in these regulations.
Reducing Risk of Model Inversion Using Privacy-Guided Training
These attacks are able to reveal the values of certain sensitive features of individuals who participated in training the model.
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