Secure, privacy-preserving and federated machine learning in medical imaging
GA Kaissis, MR Makowski, D Rückert… - Nature Machine …, 2020 - nature.com
The broad application of artificial intelligence techniques in medicine is currently hindered
by limited dataset availability for algorithm training and validation, due to the absence of …
by limited dataset availability for algorithm training and validation, due to the absence of …
Trustworthy AI: From principles to practices
The rapid development of Artificial Intelligence (AI) technology has enabled the deployment
of various systems based on it. However, many current AI systems are found vulnerable to …
of various systems based on it. However, many current AI systems are found vulnerable to …
Trustworthy LLMs: A survey and guideline for evaluating large language models' alignment
Ensuring alignment, which refers to making models behave in accordance with human
intentions [1, 2], has become a critical task before deploying large language models (LLMs) …
intentions [1, 2], has become a critical task before deploying large language models (LLMs) …
Cheetah: Lean and fast secure {Two-Party} deep neural network inference
Secure two-party neural network inference (2PC-NN) can offer privacy protection for both the
client and the server and is a promising technique in the machine-learning-as-a-service …
client and the server and is a promising technique in the machine-learning-as-a-service …
Crypten: Secure multi-party computation meets machine learning
Secure multi-party computation (MPC) allows parties to perform computations on data while
keeping that data private. This capability has great potential for machine-learning …
keeping that data private. This capability has great potential for machine-learning …
MP-SPDZ: A versatile framework for multi-party computation
M Keller - Proceedings of the 2020 ACM SIGSAC conference on …, 2020 - dl.acm.org
Multi-Protocol SPDZ (MP-SPDZ) is a fork of SPDZ-2 (Keller et al., CCS'13), an
implementation of the multi-party computation (MPC) protocol called SPDZ (Damgård et al …
implementation of the multi-party computation (MPC) protocol called SPDZ (Damgård et al …
Secure and provenance enhanced internet of health things framework: A blockchain managed federated learning approach
Recent advancements in the Internet of Health Things (IoHT) have ushered in the wide
adoption of IoT devices in our daily health management. For IoHT data to be acceptable by …
adoption of IoT devices in our daily health management. For IoHT data to be acceptable by …
Bolt: Privacy-preserving, accurate and efficient inference for transformers
The advent of transformers has brought about significant advancements in traditional
machine learning tasks. However, their pervasive deployment has raised concerns about …
machine learning tasks. However, their pervasive deployment has raised concerns about …
CryptGPU: Fast privacy-preserving machine learning on the GPU
We introduce CryptGPU, a system for privacy-preserving machine learning that implements
all operations on the GPU (graphics processing unit). Just as GPUs played a pivotal role in …
all operations on the GPU (graphics processing unit). Just as GPUs played a pivotal role in …
Cryptflow2: Practical 2-party secure inference
We present CrypTFlow2, a cryptographic framework for secure inference over realistic Deep
Neural Networks (DNNs) using secure 2-party computation. CrypTFlow2 protocols are both …
Neural Networks (DNNs) using secure 2-party computation. CrypTFlow2 protocols are both …