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 …

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 models are vulnerable to data inference attacks, such as membership
inference and model inversion attacks. In these types of breaches, an adversary attempts to …

In model extraction attacks, adversaries can steal a machine learning model exposed via a
public API by repeatedly querying it and adjusting their own model based on obtained …

As the amount of data and computational power explosively increase, valuable results are
being created using machine learning techniques. In particular, models based on deep …

Membership Inference Attack (MIA) in deep learning is a common form of privacy attack
which aims to infer whether a data sample is in a target classifier's training dataset or not …

Data holders are increasingly seeking to protect their user's privacy, whilst still maximizing
their ability to produce machine learning (ML) models with high quality predictions. In this …

Although query-based systems (QBS) have become one of the main solutions to share data
anonymously, building QBSes that robustly protect the privacy of individuals contributing to …