157 citations · 459 across the 10 of their papers we have counts for
10 papers
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…
UN Handbook on Privacy-Preserving Computation Techniques
David W. Archer, Borja de Balle Pigem, Dan Bogdanov +10
This paper describes privacy-preserving approaches for the statistical analysis. It describes motivations for privacy-preserving approaches for the statistical analysis of sensitiv…
Planting and Mitigating Memorized Content in Predictive-Text Language Models
C. M. Downey, Wei Dai, Huseyin A. Inan +3
Language models are widely deployed to provide automatic text completion services in user products. However, recent research has revealed that language models (especially large one…
Exploring Design and Governance Challenges in the Development of Privacy-Preserving Computation
Nitin Agrawal, Reuben Binns, Max Van Kleek +2
Homomorphic encryption, secure multi-party computation, and differential privacy are part of an emerging class of Privacy Enhancing Technologies which share a common promise: to pr…
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…
CHET: Compiler and Runtime for Homomorphic Evaluation of Tensor Programs
Roshan Dathathri, Olli Saarikivi, Hao Chen +5
Fully Homomorphic Encryption (FHE) refers to a set of encryption schemes that allow computations to be applied directly on encrypted data without requiring a secret key. This enabl…