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Privacy-preserving machine learning: Methods, challenges and directions
R Xu, N Baracaldo, J Joshi - ar** 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 …
{GAZELLE}: A low latency framework for secure neural network inference
The growing popularity of cloud-based machine learning raises natural questions about the
privacy guarantees that can be provided in such settings. Our work tackles this problem in …
privacy guarantees that can be provided in such settings. Our work tackles this problem in …
FAB: An FPGA-based accelerator for bootstrappable fully homomorphic encryption
Fully Homomorphic Encryption (FHE) offers protection to private data on third-party cloud
servers by allowing computations on the data in encrypted form. To support general-purpose …
servers by allowing computations on the data in encrypted form. To support general-purpose …