Public intelligent services enabled by machine learning algorithms are vulnerable to model
extraction attacks that can steal confidential information of the learning models through …

Public intelligent services enabled by machine learning algorithms are vulnerable to model
extraction attacks that can steal confidential information of the learning models through …

Abstract Machine learning models trained by large volume of proprietary data and intensive
computational resources are valuable assets of their owners, who merchandise these …

Machine learning service API allows model owners to monetize proprietary models by
offering prediction services to third-party users. However, existing literature shows that …

Machine learning and big data, foundational pillars of artificial intelligence, are propelling
the societal development, garnering significant attention. Recent studies reveal that …

The equation-solving model extraction attack is an intuitively simple but devastating attack to
steal confidential information of regression models through a sufficient number of queries …

Machine learning model is vulnerable to model extraction attacks since the attackers can
send plenty of queries to infer the hyperparameters of the machine learning model thus …

Differential privacy (DP) is a widely used approach for mitigating privacy risks when training
machine learning models on sensitive data. DP mechanisms add noise during training to …

In recent years, machine learning approaches have been widely adopted for many
applications, including classification. Machine learning models deal with collective sensitive …

Deep neural networks (DNNs) have recently been widely adopted in various applications,
and such success is largely due to a combination of algorithmic breakthroughs, computation …