SOURCE-LINKED INTELLIGENCE

Enabling Homomorphic Encryption of Deep Neural Network Models and Datasets in Production Environments

CORDIS · observation · Publication date unknown

Enabling Homomorphic Encryption of Deep Neural Network Models and Datasets in Production Environments Deep learning (DL) is widely used to solve classification problems previously unchallenged, such as face recognition, and presents clear use cases for privacy requirements. Homomorphic encryption (HE) enables operations upon encrypted data, at the expense of vast data size increase. RAM sizes currently limit the use of HE on DL to severely reduced use cases. Recently emerged persistent memory technology (PMEM) offers larger-than-ever RAM spaces, but its performance is far from that of customary DRAM technologies. This project aims at sparking a new class of system architectures for encrypted DL workloads, by eliminating or dramatically reducing data movements across memory/storage hierarchies and network, supported by PMEM technology, overcoming its current severe performance limitations. HomE intends to be a first-time enabler for the encrypted execution of large models that do not fit

Read original source ↗ Open in workspace

recordType

award

status

SIGNED

region

EU

value

2680195

unit

EUR