31 citations · 104 across the 20 of their papers we have counts for
8 papers · 1 filter
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…
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…
PriViT: Vision Transformers for Fast Private Inference
Naren Dhyani, Jianqiao Mo, Minsu Cho +4
The Vision Transformer (ViT) architecture has emerged as the backbone of choice for state-of-the-art deep models for computer vision applications. However, ViTs are ill-suited for…
DeepReShape: Redesigning Neural Networks for Efficient Private Inference
Nandan Kumar Jha, Brandon Reagen
Prior work on Private Inference (PI) -- inferences performed directly on encrypted input -- has focused on minimizing a network's ReLUs, which have been assumed to dominate PI late…
Characterizing and Optimizing End-to-End Systems for Private Inference
Karthik Garimella, Zahra Ghodsi, Nandan Kumar Jha +2
In two-party machine learning prediction services, the client's goal is to query a remote server's trained machine learning model to perform neural network inference in some applic…
Selective Network Linearization for Efficient Private Inference
Minsu Cho, Ameya Joshi, Siddharth Garg +2
Private inference (PI) enables inference directly on cryptographically secure data.While promising to address many privacy issues, it has seen limited use due to extreme runtimes.…
CryptoNite: Revealing the Pitfalls of End-to-End Private Inference at Scale
Karthik Garimella, Nandan Kumar Jha, Zahra Ghodsi +2
The privacy concerns of providing deep learning inference as a service have underscored the need for private inference (PI) protocols that protect users' data and the service provi…
Sphynx: ReLU-Efficient Network Design for Private Inference
Minsu Cho, Zahra Ghodsi, Brandon Reagen +2
The emergence of deep learning has been accompanied by privacy concerns surrounding users' data and service providers' models. We focus on private inference (PI), where the goal is…