The newly emerged machine learning (eg, deep learning) methods have become a strong
driving force to revolutionize a wide range of industries, such as smart healthcare, financial …

In recent years, data and computing resources are typically distributed in the devices of end
users, various regions or organizations. Because of laws or regulations, the distributed data …

Abstract The Randomized Response (RR) algorithm is a classical technique to improve
robustness in survey aggregation, and has been widely adopted in applications with …

We propose a reparametrization scheme to address the challenges of applying differentially
private SGD on large neural networks, which are 1) the huge memory cost of storing …

Abstract Machine unlearning is an emerging task of removing the influence of selected
training datapoints from a trained model upon data deletion requests, which echoes the …

The privacy leakage of the model about the training data can be bounded in the differential
privacy mechanism. However, for meaningful privacy parameters, a differentially private …

Differential privacy is the de-facto technique for protecting the individuals in the training
dataset and the learning models in deep learning. However, the technique presents two …

Collaborative learning has gained great popularity due to its benefit of data privacy
protection: participants can jointly train a Deep Learning model without sharing their training …

Cloud-based machine learning inference is an emerging paradigm where users query by
sending their data through a service provider who runs an ML model on that data and …

Split learning is a distributed training framework that allows multiple parties to jointly train a
machine learning model over vertically partitioned data (partitioned by attributes). The idea …