Apostolos Pyrgelis

Secure and Federated Genome-Wide Association Studies for Biobank-Scale Datasets

Jeffrey Chen, David Jules Froelicher, Jean-Pierre Hubaux, Apostolos Pyrgelis, Juan Ramón Troncoso-Pastoriza

Sharing data across multiple institutions for genome-wide association studies (GWAS) would enable discovery of novel genetic variants linked to health and disease. However, existing regulations on genomic data sharing and the sheer size of the data limit the scope of such collaborations. Although cryptographic tools for secure computation promise to enable collaborative studies with formal privacy guarantees, existing approaches either are computationally impractical or support only simplified analysis pipelines. Here, we introduce secure and federated genome-wide association studies (SF-GWAS), a novel combination of secure computation frameworks that empowers efficient and accurate GWAS in a federated manner, i.e., on private data locally-held by multiple entities, while provably ensuring end-to-end data confidentiality. Another key advance is that we designed SF-GWAS to support the two most widely-used GWAS pipelines—those based on principal component analysis (PCA) or linear mixed models (LMMs). We ran SF-GWAS on five real GWAS datasets, including a large UK Biobank cohort of 410K individuals, thereby demonstrating the largest secure genetics collaboration to date. SF-GWAS achieves an order-of-magnitude runtime improvement over the prior art for PCA-based GWAS and newly allows secure LMM-based association tests, for which its runtime scales at a near-constant rate in cohort size. Our work realizes the power of secure, collaborative, and accurate GWAS at unprecedented scale and should be applicable to a broad range of analyses. Our open-source software is at: https://github.com/hhcho/sfgwas.

2022

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

Verifiable Encodings for Secure Homomorphic Analytics

Sylvain Chatel, Jean-Pierre Hubaux, Christian Louis Knabenhans, Apostolos Pyrgelis

Homomorphic encryption, which enables the execution of arithmetic operations directly on ciphertexts, is a promising solution for protecting privacy of cloud-delegated computations on sensitive data. However, the correctness of the computation result is not ensured. We propose two error detection encodings and build authenticators that enable practical client-verification of cloud-based homomorphic computations under different trade-offs and without compromising on the features of the encryption algorithm. Our authenticators operate on top of trending ring learning with errors based fully homomorphic encryption schemes over the integers. We implement our solution in VERITAS, a ready-to-use system for verification of outsourced computations executed over encrypted data. We show that contrary to prior work VERITAS supports verification of any homomorphic operation and we demonstrate its practicality for various applications, such as ride-hailing, genomic-data analysis, encrypted search, and machine-learning training and inference.

2022

POSEIDON: Privacy-Preserving Federated Neural Network Learning

Jean-Philippe Léonard Bossuat, David Jules Froelicher, Joao André Gomes de Sá E Sousa, Jean-Pierre Hubaux, Apostolos Pyrgelis, Sinem Sav, Juan Ramón Troncoso-Pastoriza

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. It employs multiparty lattice-based cryptography to preserve the confidentiality of the training data, the model, and the evaluation data, under a passive-adversary model and collusions between up to N - 1 parties. To efficiently execute the secure backpropagation algorithm for training neural networks, we provide a generic packing approach that enables Single Instruction, Multiple Data (SIMD) operations on encrypted data. We also introduce arbitrary linear transformations within the cryptographic bootstrapping operation, optimizing the costly cryptographic computations over the parties, and we define a constrained optimization problem for choosing the cryptographic parameters. Our experimental results show that POSEIDON achieves accuracy similar to centralized or decentralized non-private approaches and that its computation and communication overhead scales linearly with the number of parties. POSEIDON trains a 3-layer neural network on the MNIST dataset with 784 features and 60K samples distributed among 10 parties in less than 2 hours.

INTERNET SOC2021

Privacy and Integrity Preserving Computations with CRISP

Sylvain Chatel, Jean-Pierre Hubaux, Apostolos Pyrgelis, Juan Ramón Troncoso-Pastoriza

In the digital era, users share their personal data with service providers to obtain some utility, e.g., access to high-quality services. Yet, the induced information flows raise privacy and integrity concerns. Consequently, cautious users may want to protect their privacy by minimizing the amount of information they disclose to curious service providers. Service providers are interested in verifying the integrity of the users' data to improve their services and obtain useful knowledge for their business. In this work, we present a generic solution to the trade-off between privacy, integrity, and utility, by achieving authenticity verification of data that has been encrypted for offloading to service providers. Based on lattice-based homomorphic encryption and commitments, as well as zero-knowledge proofs, our construction enables a service provider to process and reuse third-party signed data in a privacy-friendly manner with integrity guarantees. We evaluate our solution on different use cases such as smart-metering, disease susceptibility, and location-based activity tracking, thus showing its versatility. Our solution achieves broad generality, quantum-resistance, and relaxes some assumptions of state-of-the-art solutions without affecting performance.

USENIX ASSOC2021

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

Jean-Philippe Léonard Bossuat, David Jules Froelicher, Joao André Gomes de Sá E Sousa, 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 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

Apostolos Pyrgelis

Privacy-Preserving and Efficient Verification of the Outcome in Genome-Wide Association Studies

Secure and Federated Genome-Wide Association Studies for Biobank-Scale Datasets

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

Verifiable Encodings for Secure Homomorphic Analytics

POSEIDON: Privacy-Preserving Federated Neural Network Learning

Privacy and Integrity Preserving Computations with CRISP

SoK: Privacy-Preserving Collaborative Tree-based Model Learning

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

Privacy-Preserving and Efficient Verification of the Outcome in Genome-Wide Association Studies

Verifiable Encodings for Secure Homomorphic Analytics

Secure and Federated Genome-Wide Association Studies for Biobank-Scale Datasets

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

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

POSEIDON: Privacy-Preserving Federated Neural Network Learning

Privacy and Integrity Preserving Computations with CRISP

SoK: Privacy-Preserving Collaborative Tree-based Model Learning