Machine learning (ML) is increasingly being adopted in a wide variety of application
domains. Usually, a well-performing ML model relies on a large volume of training data and …

The trend of the next generation of the internet has already been scrutinized by top analytics
enterprises. According to Gartner investigations, it is predicted that, by 2024, 75% of the …

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

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 …

This work describes a fast fully homomorphic encryption scheme over the torus (TFHE) that
revisits, generalizes and improves the fully homomorphic encryption (FHE) based on GSW …

We present passive attacks against CKKS, the homomorphic encryption scheme for
arithmetic on approximate numbers presented at Asiacrypt 2017. The attack is both …

Homomorphic Encryption (HE) is a cryptosystem which supports computation on encrypted
data. Ló pez-Alt et al.(STOC 2012) proposed a generalized notion of HE, called Multi-Key …

In this paper, we address the problem of privacy-preserving training and evaluation of neural
networks in an $ N $-party, federated learning setting. We propose a novel system …

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