Machine learning (ML) has gained widespread adoption in a variety of fields, including
computer vision and natural language processing. However, ML models are vulnerable to …

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 …

Deep neural networks are highly susceptible to imperceptible noise, even to the human eye.
While high attack success rate has been achieved in white-box setting, the attack …

Federated learning is a privacy-preserving distributed framework that facilitates information
fusion and sharing among different clients, enabling the training of a global model without …

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

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 …

Neural network models face two highly destructive threats in real-world applications:
membership inference attacks (MIAs) and adversarial attacks (AAs). One compromises the …

The development of deep learning has brought about the business model of Machine
Learning as a Service (MLaaS). Malicious users can infer whether a member has …