5 papers
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…
Network and Compiler Optimizations for Efficient Linear Algebra Kernels in Private Transformer Inference
Karthik Garimella, Negar Neda, Austin Ebel +2
Large language model (LLM) based services are primarily structured as client-server interactions, with clients sending queries directly to cloud providers that host LLMs. This appr…
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…
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…
TruncFormer: Private LLM Inference Using Only Truncations
Patrick Yubeaton, Jianqiao Cambridge Mo, Karthik Garimella +4
Private inference (PI) serves an important role in guaranteeing the privacy of user data when interfacing with proprietary machine learning models such as LLMs. However, PI remains…