Machine learning models leak significant amount of information about their training sets,
through their predictions. This is a serious privacy concern for the users of machine learning …

In this paper (expanded from an invited talk at AISEC 2010), we discuss an emerging field of
study: adversarial machine learning---the study of effective machine learning techniques …

To promote secure and private artificial intelligence (SPAI), we review studies on the model
security and data privacy of DNNs. Model security allows system to behave as intended …

The wide adoption of Machine Learning to solve a large set of real-life problems came with
the need to collect and process large volumes of data, some of which are considered …

In recent years, machine learning (ML) has become an important part to yield security and
privacy in various applications. ML is used to address serious issues such as real-time …

This paper studies defense mechanisms against model inversion (MI) attacks--a type of
privacy attacks aimed at inferring information about the training data distribution given the …

Advances in machine learning (ML) in recent years have enabled a dizzying array of
applications such as data analytics, autonomous systems, and security diagnostics. ML is …

With the increased attention and legislation for data-privacy, collaborative machine learning
(ML) algorithms are being developed to ensure the protection of private data used for …

Machine learning is one of the most prevailing techniques in computer science, and it has
been widely applied in image processing, natural language processing, pattern recognition …

The majority of machine learning methodologies operate with the assumption that their
environment is benign. However, this assumption does not always hold, as it is often …