Cloud computing and cloud storage have contributed to a big shift in data processing and its
use. Availability and accessibility of resources with the reduction of substantial work is one of …

The new development trends including Internet of Things (IoT), smart city, enterprises digital
transformation and world's digital economy are at the top of the tide. The continuous growth …

Data privacy concerns are increasing significantly in the context of the Internet of Things,
cloud services, edge computing, artificial intelligence applications, and other applications …

Federated learning (FL) and split learning (SL) are two popular distributed machine learning
approaches. Both follow a model-to-data scenario; clients train and test machine learning …

Many companies provide neural network prediction services to users for a wide range of
applications. However, current prediction systems compromise one party's privacy: either the …

Homomorphic encryption (HE) enables the secure offloading of computations to the cloud by
providing computation on encrypted data (ciphertexts). HE is based on noisy encryption …

Indistinguishability obfuscation, introduced by [Barak et. al. Crypto 2001], aims to compile
programs into unintelligible ones while preserving functionality. It is a fascinating and …

The growing popularity of cloud-based machine learning raises natural questions about the
privacy guarantees that can be provided in such settings. Our work tackles this problem in …

Fully Homomorphic Encryption (FHE) offers protection to private data on third-party cloud
servers by allowing computations on the data in encrypted form. To support general-purpose …

Legacy encryption systems depend on sharing a key (public or private) among the peers
involved in exchanging an encrypted message. However, this approach poses privacy …