[HTML][HTML] Privacy-preserving artificial intelligence in healthcare: Techniques and applications
There has been an increasing interest in translating artificial intelligence (AI) research into
clinically-validated applications to improve the performance, capacity, and efficacy of …
clinically-validated applications to improve the performance, capacity, and efficacy of …
Membership inference attacks on machine learning: A survey
Machine learning (ML) models have been widely applied to various applications, including
image classification, text generation, audio recognition, and graph data analysis. However …
image classification, text generation, audio recognition, and graph data analysis. However …
On the opportunities and risks of foundation models
AI is undergoing a paradigm shift with the rise of models (eg, BERT, DALL-E, GPT-3) that are
trained on broad data at scale and are adaptable to a wide range of downstream tasks. We …
trained on broad data at scale and are adaptable to a wide range of downstream tasks. We …
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) …
Generative adversarial networks: A survey toward private and secure applications
Generative Adversarial Networks (GANs) have promoted a variety of applications in
computer vision and natural language processing, among others, due to its generative …
computer vision and natural language processing, among others, due to its generative …
When machine learning meets privacy: A survey and outlook
The newly emerged machine learning (eg, deep learning) methods have become a strong
driving force to revolutionize a wide range of industries, such as smart healthcare, financial …
driving force to revolutionize a wide range of industries, such as smart healthcare, financial …
Large image datasets: A pyrrhic win for computer vision?
In this paper we investigate problematic practices and consequences of large scale vision
datasets (LSVDs). We examine broad issues such as the question of consent and justice as …
datasets (LSVDs). We examine broad issues such as the question of consent and justice as …
A survey of incentive mechanism design for federated learning
Federated learning is promising in enabling large-scale machine learning by massive
clients without exposing their raw data. It can not only enable the clients to preserve the …
clients without exposing their raw data. It can not only enable the clients to preserve the …
Threats, attacks and defenses to federated learning: issues, taxonomy and perspectives
Abstract Empirical attacks on Federated Learning (FL) systems indicate that FL is fraught
with numerous attack surfaces throughout the FL execution. These attacks can not only …
with numerous attack surfaces throughout the FL execution. These attacks can not only …
Differentially private learning needs better features (or much more data)
We demonstrate that differentially private machine learning has not yet reached its" AlexNet
moment" on many canonical vision tasks: linear models trained on handcrafted features …
moment" on many canonical vision tasks: linear models trained on handcrafted features …