Privacy-preserving machine learning as a service (PP-MLaaS) can achieve the secure
model computation towards the client's private input through a series of privacy-preserving …

Deep learning plays an essential role in multidisciplinary research of remote sensing. We
will encounter security problems during the data acquisition, processing, and result …

Homomorphic Encryption (HE) is a promising technology to protect clients' data privacy for
Machine Learning as a Service (MLaaS) on public clouds. However, HE operations can be …

Privacy-preserving neural network (NN) inference solutions have recently gained significant
traction with several solutions that provide different latency-bandwidth trade-offs. Of these …

To enable common users to capitalize on the power of deep learning, Machine Learning as
a Service (MLaaS) has been proposed in the literature, which opens powerful deep learning …

With increasing concerns about privacy issues in deep learning, privacy-preserving neural
network inference has been receiving growing attention from the community, but the …

Due to the extensive application of machine learning (ML) in a wide range of fields and the
necessity of data privacy, privacy-preserving machine learning (PPML) solutions have …

Fully homomorphic encryption (FHE) offers a promising solution to ensure data privacy by
enabling computations directly on encrypted data. However, its notorious performance …

Enabling secure inference of large-scale CNNs using Homomorphic Encryption (HE)
requires a preliminary step for adapting unencrypted pre-trained models to only use …

Privacy-preserving neural network (NN) inference solutions under homomorphic encryption
(HE) have recently gained significant traction with several solutions that provide different …