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 …

Machine learning (ML) models cannot neglect risks to security, privacy, and fairness.
Several defenses have been proposed to mitigate such risks. When a defense is effective in …

Data are the critical fuel for Artificial Intelligence (AI) models. Poor quality data produces
inaccurate and ineffective AI models that may lead to incorrect or unsafe use. Checking for …

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 …

In traditional centralized Android malware classifiers based on machine learning, the
training sample uploaded by users contains sensitive personal information, such as app …

Machine learning (ML) models are costly to train as they can require a significant amount of
data, computational resources and technical expertise. Thus, they constitute valuable …

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 …

Membership inference (MI) attacks threaten user privacy through determining if a given data
example has been used to train a target model. Existing MI defenses protect the …

Membership Inference Attacks (MIAs) infer whether a data point is in the training data of a
machine learning model. It is a threat while being in the training data is private information of …

" Garbage In Garbage Out" is a universally agreed quote by computer scientists from various
domains, including Artificial Intelligence (AI). As data is the fuel for AI, models trained on low …