As machine learning becomes more widely used, the need to study its implications in
security and privacy becomes more urgent. Although the body of work in privacy has been …

Machine-Learning-as-a-Service (MLaaS) has become a widespread paradigm, making
even the most complex Machine Learning models available for clients via, eg, a pay-per …

High-performance Deep Neural Networks (DNNs) are increasingly deployed in many real-
world applications eg, cloud prediction APIs. Recent advances in model functionality …

Training machine learning (ML) models is expensive in terms of computational power,
amounts of labeled data and human expertise. Thus, ML models constitute business value …

Local differential privacy (LDP) is an emerging technique for privacy-preserving data
collection without a trusted collector. Despite its strong privacy guarantee, LDP cannot be …

Recent studies show that machine learning models are vulnerable to model extraction
attacks, where the adversary builds a substitute model that achieves almost the same …

Time series has numerous application scenarios. However, since many time series data are
personal data, releasing them directly could cause privacy infringement. All existing …

Machine learning models have achieved state-of-the-art performance in various fields, from
image classification to speech recognition. However, such models are trained with a large …

A key factor in big data analytics and artificial intelligence is the collection of user data from a
large population. However, the collection of user data comes at the price of privacy risks, not …

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