Jean-Philippe Léonard Bossuat

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Genotype imputation is a fundamental step in genomic data analysis, where missing variant genotypes are predicted using the existing genotypes of nearby "tag"variants. Although researchers can outsource genotype imputation, privacy concerns may prohibit ge ...

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 ...

We present a bootstrapping procedure for the full-RNS variant of the approximate homomorphic-encryption scheme of Cheon et al., CKKS (Asiacrypt 17, SAC 18). Compared to the previously proposed procedures (Eurocrypt 18 & 19, CT-RSA 20), our bootstrapping pr ...

We present and demo of Lattigo, a multiparty homomorphic encryption library in Go. After a brief introduction of the origin and history of the library, we dive into the most relevant technical aspects that differentiate Lattigo from other existing librarie ...

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 ...

Bootstrapping parameters for the approximate homomorphic-encryption scheme of Cheon et al., CKKS (Asiacrypt 17), are usually instantiated using sparse secrets to be efficient. However, using sparse secrets constrains the range of practical parameters withi ...

Jean-Philippe Léonard Bossuat

Chattez avec Graph Search

Ultrafast homomorphic encryption models enable secure outsourcing of genotype imputation

POSEIDON: Privacy-Preserving Federated Neural Network Learning

Efficient Bootstrapping for Approximate Homomorphic Encryption with Non-sparse Keys

Lattigo: A multiparty homomorphic encryption library in Go

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

Bootstrapping for Approximate Homomorphic Encryption with Negligible Failure-Probability by Using Sparse-Secret Encapsulation

Scalable Privacy-Preserving Distributed Learning