21 citations · 64 across the 16 of their papers we have counts for
9 papers · 1 filter
EinHops: Einsum Notation for Expressive Homomorphic Operations on RNS-CKKS Tensors
Karthik Garimella, Austin Ebel, Brandon Reagen
Fully Homomorphic Encryption (FHE) is an encryption scheme that allows for computation to be performed directly on encrypted data, effectively closing the loop on secure and outsou…
HE-LRM: Encrypted Deep Learning Recommendation Models using Fully Homomorphic Encryption
Karthik Garimella, Austin Ebel, Gabrielle De Micheli +1
Fully Homomorphic Encryption (FHE) enables computation directly on encrypted data and privacy-preserving neural inference in the cloud. Existing solutions focus on models with dens…
Osiris: A Systolic Approach to Accelerating Fully Homomorphic Encryption
Austin Ebel, Brandon Reagen
In this paper we show how fully homomorphic encryption (FHE) can be accelerated using a systolic architecture. We begin by analyzing FHE algorithms and then develop systolic or sys…
NTTSuite: Number Theoretic Transform Benchmarks for Accelerating Encrypted Computation
Juran Ding, Yuanzhe Liu, Lingbin Sun +1
Privacy concerns have thrust privacy-preserving computation into the spotlight. Homomorphic encryption (HE) is a cryptographic system that enables computation to occur directly on…
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…
Privacy Preserving In-memory Computing Engine
Haoran Geng, Jianqiao Mo, Dayane Reis +5
Privacy has rapidly become a major concern/design consideration. Homomorphic Encryption (HE) and Garbled Circuits (GC) are privacy-preserving techniques that support computations o…
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…
TREBUCHET: Fully Homomorphic Encryption Accelerator for Deep Computation
David Bruce Cousins, Yuriy Polyakov, Ahmad Al Badawi +21
Secure computation is of critical importance to not only the DoD, but across financial institutions, healthcare, and anywhere personally identifiable information (PII) is accessed.…