175 citations · 262 across the 24 of their papers we have counts for
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cs.CR2021★ 25 cited
Does Fully Homomorphic Encryption Need Compute Acceleration?
Leo de Castro, Rashmi Agrawal, Rabia Yazicigil +4
Fully Homomorphic Encryption (FHE) allows arbitrarily complex computations on encrypted data without ever needing to decrypt it, thus enabling us to maintain data privacy on third-…
cs.CR2021★ 2 cited
NeuraCrypt: Hiding Private Health Data via Random Neural Networks for Public Training
Adam Yala, Homa Esfahanizadeh, Rafael G. L. D' Oliveira +6
Balancing the needs of data privacy and predictive utility is a central challenge for machine learning in healthcare. In particular, privacy concerns have led to a dearth of public…
cs.LG2021
The Fine-Grained Hardness of Sparse Linear Regression
Aparna Gupte, Vinod Vaikuntanathan
Sparse linear regression is the well-studied inference problem where one is given a design matrix and a response vector $\mathbf{b} \in \mat…