Truly privacy-preserving federated analytics for precision medicine with multiparty homomorphic encryption

Abstract

Using real-world evidence in biomedical research, an indispensable complement to clinical trials, requires access to large quantities of patient data that are typically held separately by multiple healthcare institutions. We propose FAMHE, a novel federated analytics system that, based on multiparty homomorphic encryption (MHE), enables privacy-preserving analyses of distributed datasets by yielding highly accurate results without revealing any intermediate data. We demonstrate the applicability of FAMHE to essential biomedical analysis tasks, including Kaplan-Meier survival analysis in oncology and genome-wide association studies in medical genetics. Using our system, we accurately and efficiently reproduce two published centralized studies in a federated setting, enabling biomedical insights that are not possible from individual institutions alone. Our work represents a necessary key step towards overcoming the privacy hurdle in enabling multi-centric scientific collaborations.

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

“…Finally, HE-based solutions for privacy-preserving analytics in distributed medical settings 32 , 33 allow for functionalities (e.g., basic statistics, counting, or linear regression) different than those that PriCell enables, and they do not enable the efficient execution of neural networks in an FL setting. Due to the fact that the underlying system, the threat model, and the enabled functionalities of all the aforementioned solutions are different from PriCell , a quantitative comparison with these works is a challenging task.…”

“…To balance these trade-offs, several works employ multiparty homomorphic encryption (MHE). 32 , 33 Although the underlying model in these solutions enables privacy-preserving distributed computations and maintains the local data of the parties on their local premises, the functionality of these works is limited to the execution of simple operations, i.e., basic statistics, counting, or linear regression, and the underlying protocols do not support an efficient execution of neural networks in the FL setting.…”

Privacy-Preserving Federated Neural Network Learning for Disease-Associated Cell Classification

“…Lastly, HE-based solutions for privacy-preserving analytics in distributed medical settings 31, 32 allow for functionalities (e.g., basic statistics, counting, or linear regression) different than those PriCell enables, and they do not enable the efficient execution of neural networks in a federated learning setting. Due to the fact that the underlying system, the threat model, and the enabled functionalities of all the aforementioned solutions are different from PriCell , a quantitative comparison with these works is a challenging task.…”

“…The adoption of each of the aforementioned solutions introduces several privacy, utility, and performance trade-offs that need to be carefully balanced for healthcare applications. To balance these trade-offs, several works employ multiparty homomorphic encryption (MHE) 31,32 . Although the underlying model in these solutions enables privacy-preserving distributed computations and maintains the local data of the parties on their local premises, the functionality of these works is limited to the execution of simple operations, i.e., basic statistics, counting, or linear regression and the underlying protocols do not support an efficient execution of neural networks in the federated learning setting.…”

Secure federated Boolean count queries using fully-homomorphic cryptography

“…On the cryptographic theory side, alternate FHE protocols with better complexity were developed, as well as extensions of the key-generation protocols to allow for multi-party encryption and decryption [11,12,13,14]. Practical implementations of RLWE based FHE protocols are just beginning to become available, yet they have already been utilized in the computational biology community to craft secure versions of existing algorithm often run on sensitive data, most notably secure GWAS in a 2020 PNAS article [15], as well as a more recent 2021 Nature Communications paper [16].…”