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 …

Deep neural networks (DNNs) have recently been widely adopted in various applications,
and such success is largely due to a combination of algorithmic breakthroughs, computation …

In the field of data mining, protecting sensitive data from being leaked is part of the focuses
of current research. As a strict and provable definition of privacy model, differential privacy …

With the development of machine learning (ML) technology, various online intelligent
services use ML models to provide predictions. However, attacker may obtain privacy …

Machine learning models are increasingly made available to the masses through public
query interfaces. Recent academic work has demonstrated that malicious users who can …

Major cloud service providers with well-equipped infrastructure, experienced machine
learning (ML) expertise, and enriched training datasets are building ML-as-a-Service …

Practitioners commonly download pretrained machine learning models from open
repositories and finetune them to fit specific applications. We show that this practice …

The unprecedented success of deep learning is largely dependent on the availability of
massive amount of training data. In many cases, these data are crowd-sourced and may …

Counterfactual, serving as one emerging type of model explanation, has attracted tons of
attentions recently from both industry and academia. Different from the conventional feature …

The growing number of mobile and IoT devices has nourished many intelligent applications.
In order to produce high-quality machine learning models, they constantly access and …