Publications by Sinem Sav | EPFL Graph Search

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Homomorphic encryption (HE), which allows computations on encrypted data, is an enabling technology for confidential cloud computing. One notable example is privacy-preserving Prediction-as-a-Service (PaaS), where machine-learning predictions are computed ...

In this paper, we address the problem of privacy-preserving training and evaluation of neural networks in an N-party, federated learning setting. We propose a novel system, POSEIDON, the first of its kind in the regime of privacy-preserving neural network ...

Training accurate and robust machine learning models requires a large amount of data that is usually scattered across data silos. Sharing, transferring, and centralizing the data from silos, however, is difficult due to current privacy regulations (e.g., H ...

Training accurate and robust machine learning models requires a large amount of data that is usually scattered across data silos. Sharing or centralizing the data of different healthcare institutions is, however, unfeasible or prohibitively difficult due t ...

A computer-implemented method and a distributed computer system (100) for privacy- preserving distributed training of a global model on distributed datasets (DS1 to DSn). The system has a plurality of data providers (DP1 to DPn) being communicatively coupl ...

A computer-implemented method and a distributed computer system (100) for privacy- preserving distributed training of a global neural network model on distributed datasets (DS1 to DSn). The system has a plurality of data providers (DP1 to DPn) being commun ...

Sinem Sav

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slytHErin: An Agile Framework for Encrypted Deep Neural Network Inference

POSEIDON: Privacy-Preserving Federated Neural Network Learning

Privacy-preserving federated neural network training and inference

Privacy-preserving federated neural network learning for disease-associated cell classification

System and method for privacy-preserving distributed training of machine learning models on distributed datasets

System and method for privacy-preserving distributed training of neural network models on distributed datasets

Scalable Privacy-Preserving Distributed Learning