Privacy-preserving neural networks with homomorphic encryption: C hallenges and opportunities
Classical machine learning modeling demands considerable computing power for internal
calculations and training with big data in a reasonable amount of time. In recent years …
calculations and training with big data in a reasonable amount of time. In recent years …
Privacy-Preserving Data-Driven Learning Models for Emerging Communication Networks: A Comprehensive Survey
With the proliferation of Beyond 5G (B5G) communication systems and heterogeneous
networks, mobile broadband users are generating massive volumes of data that undergo …
networks, mobile broadband users are generating massive volumes of data that undergo …
On the security of homomorphic encryption on approximate numbers
We present passive attacks against CKKS, the homomorphic encryption scheme for
arithmetic on approximate numbers presented at Asiacrypt 2017. The attack is both …
arithmetic on approximate numbers presented at Asiacrypt 2017. The attack is both …
A full RNS variant of approximate homomorphic encryption
Abstract The technology of Homomorphic Encryption (HE) has improved rapidly in a few
years. The newest HE libraries are efficient enough to use in practical applications. For …
years. The newest HE libraries are efficient enough to use in practical applications. For …
Over 100x faster bootstrap** in fully homomorphic encryption through memory-centric optimization with GPUs
Fully Homomorphic encryption (FHE) has been gaining in popularity as an emerging means
of enabling an unlimited number of operations in an encrypted message without decryption …
of enabling an unlimited number of operations in an encrypted message without decryption …
SHARP: A short-word hierarchical accelerator for robust and practical fully homomorphic encryption
Fully homomorphic encryption (FHE) is an emerging cryptographic technology that
guarantees the privacy of sensitive user data by enabling direct computations on encrypted …
guarantees the privacy of sensitive user data by enabling direct computations on encrypted …
Chimera: Combining ring-lwe-based fully homomorphic encryption schemes
This paper proposes a practical hybrid solution for combining and switching between three
popular Ring-LWE-based FHE schemes: TFHE, B/FV and HEAAN. This is achieved by first …
popular Ring-LWE-based FHE schemes: TFHE, B/FV and HEAAN. This is achieved by first …
A framework for collaborative learning in secure high-dimensional space
As the amount of data generated by the Internet of the Things (IoT) devices keeps
increasing, many applications need to offload computation to the cloud. However, it often …
increasing, many applications need to offload computation to the cloud. However, it often …
Verifiable fully homomorphic encryption
Fully Homomorphic Encryption (FHE) is seeing increasing real-world deployment to protect
data in use by allowing computation over encrypted data. However, the same malleability …
data in use by allowing computation over encrypted data. However, the same malleability …
Fed-cbs: A heterogeneity-aware client sampling mechanism for federated learning via class-imbalance reduction
Due to the often limited communication bandwidth of edge devices, most existing federated
learning (FL) methods randomly select only a subset of devices to participate in training at …
learning (FL) methods randomly select only a subset of devices to participate in training at …