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Shao-Wei Chiu's repositories

Accelerator-Architectures-for-Machine-Learning

2020 NCTU Accelerator Architectures for Machine Learning

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Game-Theory

Term-Project of Game Theory

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Machine-Learning

2020 NCTU Machine Learning

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CryptoNets is a demonstration of the use of Neural-Networks over data encrypted with Homomorphic Encryption. Homomorphic Encryptions allow performing operations such as addition and multiplication over data while it is encrypted. Therefore, it allows keeping data private while outsourcing computation (see here and here for more about Homomorphic Encryptions and its applications). This project demonstrates the use of Homomorphic Encryption for outsourcing neural-network predictions. The scenario in mind is a provider that would like to provide Prediction as a Service (PaaS) but the data for which predictions are needed may be private. This may be the case in fields such as health or finance. By using CryptoNets, the user of the service can encrypt their data using Homomorphic Encryption and send only the encrypted message to the service provider. Since Homomorphic Encryptions allow the provider to operate on the data while it is encrypted, the provider can make predictions using a pre-trained Neural-Network while the data remains encrypted throughout the process and finaly send the prediction to the user who can decrypt the results. During the process the service provider does not learn anything about the data that was used, the prediction that was made or any intermediate result since everything is encrypted throughout the process. This project uses the Simple Encrypted Arithmetic Library SEAL version 3.2.1 implementation of Homomorphic Encryption developed in Microsoft Research.

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delphi

A Cryptographic Inference Service for Neural Networks

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excitationbp

Visualizing how deep networks make decisions

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EzPC

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face-alignment

:fire: 2D and 3D Face alignment library build using pytorch

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facenet-pytorch-vggface2

A PyTorch implementation of the 'FaceNet' paper for training a facial recognition model with Triplet Loss using the VGGFace2 dataset. A pre-trained model using Triplet Loss is available for download.

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