Machine learning and statistical techniques are powerful tools for analyzing large amounts
of medical and genomic data. On the other hand, ethical concerns and privacy regulations …

It is unlikely that a hacker is able to compromise sensitive data that is stored in an encrypted
form. However, when data is to be processed, it has to be decrypted, becoming vulnerable to …

Fully Homomorphic Encryption (FHE) enables offloading computation to untrusted servers
with cryptographic privacy. Despite its attractive security, FHE is not yet widely adopted due …

Fully Homomorphic Encryption (FHE) allows computing on encrypted data, enabling secure
offloading of computation to untrusted servers. Though it provides ideal security, FHE is …

The electronic sharing of medical imaging data is an important element of modern
healthcare systems, but current infrastructure for cross-site image transfer depends on trust …

A recent security model for fully homomorphic encryption (FHE), called IND-CPAD security
and introduced by Li and Micciancio [Eurocrypt'21], strengthens IND-CPA security by giving …

With the rapid increase in cloud computing, concerns surrounding data privacy, security, and
confidentiality also have been increased significantly. Not only cloud providers are …

Nowadays, multimedia is considered to be the biggest big data as it dominates the traffic in
the Internet and mobile phones. Currently symmetric encryption algorithms are used in IoT …

Fully Homomorphic Encryption (FHE) is a technique that allows computation on encrypted
data. It has the potential to drastically change privacy considerations in the cloud, but high …

The rapid development of cloud computing promotes a wide deployment of data and
computation outsourcing to cloud service providers by resource-limited entities. Based on a …