Fully Homomorphic Encryption (FHE) is a powerful cryptographic primitive that enables
performing computations over encrypted data without having access to the secret key. We …

Privacy and Homomorphic Encryption are effective ways for securely evaluating network
records by encrypting them with a sophisticated cryptographic algorithm. This method …

We focus on the problem of constructing fully homomorphic encryption (FHE) schemes that
achieve some meaningful notion of adaptive chosen-ciphertext security beyond CCA1 …

Purpose The objective of this research is to explore the applicability of machine learning and
fully homomorphic encryption (FHE) in the private pathological assessment, with a focus on …

TFHE is a practical fully homomorphic encryption scheme (FHE) capable of computing any
boolean gate or non-linear function. The scheme was originally designed to work for the …

Networked systems are increasingly the target of cyberattacks that exploit vulnerabilities
within digital communications, embedded hardware, and software. Arguably, the simplest …

Data sharing is central to a wide variety of applications such as fraud detection, ad matching,
and research. The lack of data sharing abstractions makes the solution to each data sharing …

Abstract Machine learning technologies have the potential to transform and revolutionize
various industries, such as drug discovery by finding new molecules, medical diagnosis by …

The proliferation of healthcare data has expanded opportunities for collaborative research,
yet stringent privacy regulations hinder pooling sensitive patient records. We propose …

Cancer research has entered a new era with the advent of big data and advanced
computational analytics. However, the utilization of such medical data poses significant …