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 …

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 …

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

Rapid advances in quantum computing, together with the announcement by the National
Institute of Standards and Technology (NIST) to define new standards for digitalsignature …

Abstract The National Institute of Standards and Technology is in the process of selecting
publickey cryptographic algorithms through a public, competition-like process. The new …

At IEEE Security & Privacy 2015, Bos, Costello, Naehrig, and Stebila proposed an
instantiation of Peikert's ring-learning-with-errors–based (Ring-LWE) key exchange protocol …

This document is the supporting documentation of Falcon. It is organized as follows. Chapter
2 explains the overall design of Falcon and its rationale. Chapter 3 is a complete …

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

Deep Learning as a Service (DLaaS) stands as a promising solution for cloud-based
inference applications. In this setting, the cloud has a pre-learned model whereas the user …

Homomorphic encryption is a tool that enables computation on encrypted data and thus has
applications in privacy-preserving cloud computing. Though conceptually amazing …