Laboratoire de systemes decentralises et distribues

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Publications associées (62)

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

Jean-Philippe Léonard Bossuat, David Jules Froelicher, Jean-Pierre Hubaux, Apostolos Pyrgelis, Sinem Sav, Juan Ramón Troncoso-Pastoriza

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 communicatively coupled. Each data provider has a respective local training dataset (DS1 to DSn) and a vector of output labels (OL1 to OLn) for training the global model. Further, it has a portion of a cryptographic distributed secret key (SK1 to SKn) and a corresponding collective cryptographic public key (CPK) of a multiparty fully homomorphic encryption scheme, with the weights of the global model being encrypted with the collective public key. Each data provider (DP1) computes and aggregates, for each layer of the global model, encrypted local gradients (LG1) using the respective local training dataset (DS1) and output labels (OL1), with forward pass and backpropagation using stochastic gradient descent. At least one data provider homomorphically combines at least a subset of the current local gradients of at least a subset of the data providers into combined local gradients, and updates the weights of the current global model (GM) based on the combined local gradients.

2022

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Ultrafast homomorphic encryption models enable secure outsourcing of genotype imputation

Jean-Philippe Léonard Bossuat, David Jules Froelicher, Jean-Pierre Hubaux, Yiping Ma, Juan Ramón Troncoso-Pastoriza

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 genetic data sharing with an untrusted imputation service. Here, we developed secure genotype imputation using efficient homomorphic encryption (HE) techniques. In HE-based methods, the genotype data are secure while it is in transit, at rest, and in analysis. It can only be decrypted by the owner. We compared secure imputation with three state-of-the-art non-secure methods and found that HE-based methods provide genetic data security with comparable accuracy for common variants. HE-based methods have time and memory requirements that are comparable or lower than those for the non-secure methods. Our results provide evidence that HE-based methods can practically perform resource-intensive computations for high-throughput genetic data analysis. The source code is freely available for download at https://github.com/K-miran/secure-imputation.

CELL PRESS2021

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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 coupled. Each data provider has a respective local model (LM1 to LMn) and a respective local training dataset (DS1 to DSn) for training the local model using an iterative training algorithm (IA). Further it has a portion of a cryptographic distributed secret key (SK1 to SKn) and a corresponding collective cryptographic public key (CPK) of a multiparty fully homomorphic encryption scheme, with the local and global model being encrypted with the collective public key. Each data provider (DP1) trains its local model (LM1) using the respective local training dataset (DS1) by executing gradient descent updates of its local model (LM1), and combining (1340) the updated local model (LM1') with the current global model (GM) into a current local model (LM1c). At least one data provider homomorphically combines at least a subset of the current local models of at least a subset of the data providers into a combined model (CM1), and updates the current global model (GM) based on the combined model. The updated global model is provided to at least a subset of the other data providers.

2021

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