English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Deep Learning as a Service (DLaaS) stands as a promising solution forcloud-based inference applications. In this setting, the cloud has apre-learned model whereas the user has samples on which she wants to run themodel. The biggest concern with DLaaS is user privacy if the input samples aresensitive data. We provide here an efficient privacy-preserving system byemploying high-end technologies such as Fully Homomorphic Encryption (FHE),Convolutional Neural Networks (CNNs) and Graphics Processing Units (GPUs). FHE,with its widely-known feature of computing on encrypted data, empowers a widerange of privacy-concerned applications. This comes at high cost as it requiresenormous computing power. In this paper, we show how to accelerate theperformance of running CNNs on encrypted data with GPUs. We evaluated two CNNsto classify homomorphically the MNIST and CIFAR-10 datasets. Our solutionachieved a sufficient security level (> 80 bit) and reasonable classificationaccuracy (99%) and (77.55%) for MNIST and CIFAR-10, respectively. In terms oflatency, we could classify an image in 5.16 seconds and 304.43 seconds forMNIST and CIFAR-10, respectively. Our system can also classify a batch ofimages (> 8,000) without extra overhead.

Towards the AlexNet Moment for Homomorphic Encryption: HCNN, theFirst Homomorphic CNN on Encrypted Data with GPUs