In secure machine learning inference, most of the schemes assume that the server is semi-
honest (honestly following the protocol but attempting to infer additional information) …

Secure inference allows a model owner (or, the server) and the input owner (or, the client) to
perform inference on machine learning model without revealing their private information to …

The increasing adoption of machine learning inference in applications has led to a
corresponding increase in concerns about the privacy guarantees offered by existing …

Secure machine learning (ML) inference can provide meaningful privacy guarantees to both
the client (holding sensitive input) and the server (holding sensitive weights of the ML …

Machine learning is widely used to produce models for a range of applications and is
increasingly offered as a service by major technology companies. However, the required …

We introduce a new class of attacks on machine learning models. We show that an
adversary who can poison a training dataset can cause models trained on this dataset to …

Privacy-preserving inference in edge computing paradigms encourages the users of
machine-learning services to locally run a model on their private input, for a target task, and …

Training machine learning models on data from multiple entities without direct data sharing
can unlock applications otherwise hindered by business, legal, or ethical constraints. In this …

Abstract Machine Learning as a Service (MLaaS) is an increasingly popular design where a
company with abundant computing resources trains a deep neural network and offers query …

Abstract Machine Learning has been widely applied in practice, such as disease diagnosis,
target detection. Commonly, a good model relies on massive training data collected from …