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Converting Transformers to Polynomial Form for Secure Inference Over Homomorphic Encryption
This innovation enables us to perform secure inference on LMs with WikiText-103.
Privacy-Preserving Federated Learning over Vertically and Horizontally Partitioned Data for Financial Anomaly Detection
The effective detection of evidence of financial anomalies requires collaboration among multiple entities who own a diverse set of data, such as a payment network system (PNS) and its partner banks.
Efficient Skip Connections Realization for Secure Inference on Encrypted Data
Homomorphic Encryption (HE) is a cryptographic tool that allows performing computation under encryption, which is used by many privacy-preserving machine learning solutions, for example, to perform secure classification.
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.
HE-PEx: Efficient Machine Learning under Homomorphic Encryption using Pruning, Permutation and Expansion
In this work, we propose a novel set of pruning methods that reduce the latency and memory requirement, thus bringing the effectiveness of plaintext pruning methods to HE.
A methodology for training homomorphicencryption friendly neural networks
Experiments using our approach reduced the gap between the F1 score and accuracy of the models trained with ReLU and the HE-friendly model to within a mere 0. 32-5. 3 percent degradation.
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.
