Recommender systems (RS) play an integral role in many online platforms. Exponential
growth and potential commercial interests are raising significant concerns around privacy …

Split Learning (SL) has emerged as a practical and efficient alternative to traditional
federated learning. While previous attempts to attack SL have often relied on overly strong …

Privacy laws like GDPR necessitate effective approaches to safeguard data privacy. Existing
works on data privacy protection of DNNs mainly concentrated on the model training phase …

Federated learning (FL) enables collaborative model training without sharing private data,
thereby potentially meeting the growing demand for data privacy protection. Despite its …

Facing enormous data generated at the network edge, Edge Intelligence (EI) emerges as
the fusion of Edge Computing and Artificial Intelligence, revolutionizing edge data …

联邦学习作为一种分布式机器学习技术可以解决数据孤岛问题, 但机器学习模型会无意识地记忆
训练数据, 导致参与方上传的模型参数与全局模型会遭受各种隐私攻击. 针对隐私攻击中的模型 …

Mobile Edge Computing (MEC) has great potential to facilitate cheap and fast customer
behavior analysis (CBA). Model splitting, widely adopted in collaborative learning of MEC …

A Model Inversion (MI) attack based on Generative Adversarial Networks (GAN) aims to
recover the private training data from complex deep learning models by searching codes in …

Vertical Federated Learning (VFL) facilitates collaborative machine learning without the
need for participants to share raw private data. However, recent studies have revealed …

Collaborative inference accelerates DNN inference tasks of resource-limited devices (eg,
clients) by offloading model slices to resource-rich devices (eg, servers). During the …