157 citations · 459 across the 10 of their papers we have counts for
6 papers · 1 filter
Does Prompt-Tuning Language Model Ensure Privacy?
Shangyu Xie, Wei Dai, Esha Ghosh +3
Prompt-tuning has received attention as an efficient tuning method in the language domain, i.e., tuning a prompt that is a few tokens long, while keeping the large language model f…
Trustworthy AI Inference Systems: An Industry Research View
Rosario Cammarota, Matthias Schunter, Anand Rajan +26
In this work, we provide an industry research view for approaching the design, deployment, and operation of trustworthy Artificial Intelligence (AI) inference systems. Such systems…
EVA: An Encrypted Vector Arithmetic Language and Compiler for Efficient Homomorphic Computation
Roshan Dathathri, Blagovesta Kostova, Olli Saarikivi +3
Fully-Homomorphic Encryption (FHE) offers powerful capabilities by enabling secure offloading of both storage and computation, and recent innovations in schemes and implementations…
HEAX: An Architecture for Computing on Encrypted Data
M. Sadegh Riazi, Kim Laine, Blake Pelton +1
With the rapid increase in cloud computing, concerns surrounding data privacy, security, and confidentiality also have been increased significantly. Not only cloud providers are su…
PrivFT: Private and Fast Text Classification with Homomorphic Encryption
Ahmad Al Badawi, Luong Hoang, Chan Fook Mun +2
The need for privacy-preserving analytics is higher than ever due to the severity of privacy risks and to comply with new privacy regulations leading to an amplified interest in pr…
XONN: XNOR-based Oblivious Deep Neural Network Inference
M. Sadegh Riazi, Mohammad Samragh, Hao Chen +3
Advancements in deep learning enable cloud servers to provide inference-as-a-service for clients. In this scenario, clients send their raw data to the server to run the deep learni…