Graph neural networks (GNNs) are an emerging research field. This specialized deep
neural network architecture is capable of processing graph structured data and bridges the …

The outstanding performance of deep neural networks has promoted deep learning
applications in a broad set of domains. However, the potential risks caused by adversarial …

Federated learning is a promising distributed machine learning paradigm that has been
playing a significant role in providing privacy-preserving learning solutions. However …

Deep generative models have gained much attention given their ability to generate data for
applications as varied as healthcare to financial technology to surveillance, and many more …

Differential privacy has been a de facto privacy standard in defining privacy and handling
privacy preservation. It has had great success in scenarios of local data privacy and …

In this paper, we focus on the privacy preserving mechanism design for crowdsourced
Federated Learning (FL), where a requester can outsource its model training task to some …

Advanced persistent threats (APTs) are one of today's major threats to cyber security. Highly
determined attackers along with novel and evasive exfiltration techniques mean APT attacks …

A cyber system may face to multiple attackers from diverse adversaries, who usually employ
sophisticated techniques to both continuously steal sensitive data and avoid being detected …

Collaborative machine learning involves training models on data from multiple parties but
must incentivize their participation. Existing data valuation methods fairly value and reward …

Mobile crowdsensing (MCS) systems typically struggle to address the challenge of data
aggregation, incentive design, and privacy protection, simultaneously. However, existing …