A Lightweight, Anonymous and Confidential Genomic Computing for Industrial Scale Deployment

10/04/2021
by   Huafei Zhu, et al.
0

This paper studies anonymous and confidential genomic case and control computing within the federated framework leveraging SPDZ. Our contribution mainly comprises the following three-fold: * In the first fold, an efficient construction of Beaver triple generators (BTGs) formalized in the 3-party computation leveraging multiplicatively homomorphic key management protocols (mHKMs) is presented and analysed. Interestingly, we are able to show the equivalence between BTGs and mHKMs. We then propose a lightweight construction of BTGs, and show that our construction is secure against semi-honest adversary if the underlying multiplicatively homomorphic encryption is semantically secure. * In the second fold, a decoupling model for SPDZ with explicit separation of BTGs from MPC servers (MPCs) is introduced and formalized, where BTGs aim to generate the Beaver triples while MPCs to process the input data. A new notion, which we call blind triple dispensation protocol, is then introduced for securely dispensing the generated Beaver triples, and constructed from mHKMs. We demonstrate the power of mHKMs by showing that it is a useful notion not only for generating Beaver triples but also for securely dispensing triples as well. * In the third-fold, a lightweight genomic case and control computing model is proposed, which reaches the anonymity and confidentiality simultaneously. An efficient truncation algorithm leveraging the depicted BTGs above is then proposed by eliminating computational cost heavy PRandBitL() and PRandInt() protocols involved in the state-of-the-art solutions and thus largely benefits us computing residual vectors for industrial scale deployment.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
02/13/2022

Highly Scalable Beaver Triple Generator from Additive-only Homomorphic Encryption

In a convolution neural network, a composition of linear scalar product,...
research
03/17/2020

Privacy-preserving Weighted Federated Learning within Oracle-Aided MPC Framework

This paper studies privacy-preserving weighted federated learning within...
research
10/16/2020

Barrington Plays Cards: The Complexity of Card-based Protocols

In this paper we study the computational complexity of functions that ha...
research
08/06/2020

On the relationship between (secure) multi-party computation and (secure) federated learning

The contribution of this short note, contains the following two parts: i...
research
10/29/2019

Secure and Efficient Federated Transfer Learning

Machine Learning models require a vast amount of data for accurate train...

Please sign up or login with your details

Forgot password? Click here to reset