A comprehensive survey of privacy-preserving federated learning: A taxonomy, review, and future directions
The past four years have witnessed the rapid development of federated learning (FL).
However, new privacy concerns have also emerged during the aggregation of the …
However, new privacy concerns have also emerged during the aggregation of the …
[HTML][HTML] Privacy-preserving neural networks with homomorphic encryption: C hallenges and opportunities
B Pulido-Gaytan, A Tchernykh… - Peer-to-Peer Networking …, 2021 - Springer
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 …
Cheetah: Lean and fast secure {Two-Party} deep neural network inference
Secure two-party neural network inference (2PC-NN) can offer privacy protection for both the
client and the server and is a promising technique in the machine-learning-as-a-service …
client and the server and is a promising technique in the machine-learning-as-a-service …
Craterlake: a hardware accelerator for efficient unbounded computation on encrypted data
Fully Homomorphic Encryption (FHE) enables offloading computation to untrusted servers
with cryptographic privacy. Despite its attractive security, FHE is not yet widely adopted due …
with cryptographic privacy. Despite its attractive security, FHE is not yet widely adopted due …
F1: A fast and programmable accelerator for fully homomorphic encryption
Fully Homomorphic Encryption (FHE) allows computing on encrypted data, enabling secure
offloading of computation to untrusted servers. Though it provides ideal security, FHE is …
offloading of computation to untrusted servers. Though it provides ideal security, FHE is …
Delphi: A cryptographic inference system for neural networks
Many companies provide neural network prediction services to users for a wide range of
applications. However, current prediction systems compromise one party's privacy: either the …
applications. However, current prediction systems compromise one party's privacy: either the …
Survey on fully homomorphic encryption, theory, and applications
Data privacy concerns are increasing significantly in the context of the Internet of Things,
cloud services, edge computing, artificial intelligence applications, and other applications …
cloud services, edge computing, artificial intelligence applications, and other applications …
Bts: An accelerator for bootstrappable fully homomorphic encryption
Homomorphic encryption (HE) enables the secure offloading of computations to the cloud by
providing computation on encrypted data (ciphertexts). HE is based on noisy encryption …
providing computation on encrypted data (ciphertexts). HE is based on noisy encryption …
Low-complexity deep convolutional neural networks on fully homomorphic encryption using multiplexed parallel convolutions
Recently, the standard ResNet-20 network was successfully implemented on the fully
homomorphic encryption scheme, residue number system variant Cheon-Kim-Kim-Song …
homomorphic encryption scheme, residue number system variant Cheon-Kim-Kim-Song …
A study of face obfuscation in imagenet
Face obfuscation (blurring, mosaicing, etc.) has been shown to be effective for privacy
protection; nevertheless, object recognition research typically assumes access to complete …
protection; nevertheless, object recognition research typically assumes access to complete …