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

Privacy has gained a growing interest nowadays due to the increasing and unmanageable
amount of produced confidential data. Concerns about the possibility of sharing data with …

In the cloud computing era, privacy protection is becoming pervasive in a broad range of
applications (eg, machine learning, data mining, etc). Fully Homomorphic Encryption (FHE) …

Outsourced computation for neural networks allows users access to state-of-the-art models
without investing in specialized hardware and know-how. The problem is that the users lose …

Fully Homomorphic Encryption (FHE) enables computing on encrypted data, letting clients
securely offload computation to untrusted servers. While enticing, FHE has two key …

Abstract 5G heterogeneous networks have become a promising platform to connect a
growing number of Internet-of-Things (IoT) devices and accommodate a wide variety of …

Embeddings, which compress information in raw text into semantics-preserving low-
dimensional vectors, have been widely adopted for their efficacy. However, recent research …

Ring-Learning-with-Errors (RLWE) has emerged as the foundation of many important
techniques for improving security and privacy, including homomorphic encryption and post …

Recently large language models (LLMs) have gained significant attention as they have
shown surprising signs of artificial general intelligence (AGI). Artificial intelligence and large …

Fully homomorphic encryption (FHE) is a cryptographic technology capable of resolving
security and privacy problems in cloud computing by encrypting data in use. However, FHE …