Machine learning (ML) models have been widely applied to various applications, including
image classification, text generation, audio recognition, and graph data analysis. However …

A crucial module of the widely applied machine learning (ML) model is the model training
phase, which involves large-scale training data, often including sensitive private data. ML …

Collaborative inference (co-inference) accelerates deep neural network inference via
extracting representations at the device and making predictions at the edge server, which …

Transfer learning has become a prevailing machine learning technique thanks to its
superiority in learning knowledge from limited training data for prediction. In the existing …

In today's machine learning (ML) models, any part of the training data can affect the model
output. This lack of control for information flow from training data to model output is a major …

Membership inference (MI) attacks mainly aim to infer whether a data record was used to
train a target model or not. Due to the serious privacy risks, MI attacks have been attracting a …

Preserving privacy is a growing concern in our society where cameras are ubiquitous. In this
work, we propose a trainable image acquisition method that removes the sensitive …

Transfer learning, successful in knowledge translation across related tasks, faces a
substantial privacy threat from membership inference attacks (MIAs). These attacks, despite …

Membership inference (MI) attacks threaten user privacy through determining if a given data
example has been used to train a target model. However, it has been increasingly …

摘要机器学习被广泛应用于各个领域, 已成为推动各行业革命的强大动力,
极大促进了人工智能的繁荣与发展. 同时, 机器学习模型的训练和预测均需要大量数据 …