4 citations · 7 across the 8 of their papers we have counts for
3 papers · 1 filter
Orion: A Fully Homomorphic Encryption Framework for Deep Learning
Austin Ebel, Karthik Garimella, Brandon Reagen
Fully Homomorphic Encryption (FHE) has the potential to substantially improve privacy and security by enabling computation directly on encrypted data. This is especially true with…
CiFlow: Dataflow Analysis and Optimization of Key Switching for Homomorphic Encryption
Negar Neda, Austin Ebel, Benedict Reynwar +1
Homomorphic encryption (HE) is a privacy-preserving computation technique that enables computation on encrypted data. Today, the potential of HE remains largely unrealized as it is…
Towards Fast and Scalable Private Inference
Jianqiao Mo, Karthik Garimella, Negar Neda +2
Privacy and security have rapidly emerged as first order design constraints. Users now demand more protection over who can see their data (confidentiality) as well as how it is use…