43 citations · 249 across the 65 of their papers we have counts for
6 papers · 1 filter
Identifying Hidden Buyers in Darknet Markets via Dirichlet Hawkes Process
Panpan Zheng, Shuhan Yuan, Xintao Wu +1
The darknet markets are notorious black markets in cyberspace, which involve selling or brokering drugs, weapons, stolen credit cards, and other illicit goods. To combat illicit tr…
Fairness through Equality of Effort
Wen Huang, Yongkai Wu, Lu Zhang +1
Fair machine learning is receiving an increasing attention in machine learning fields. Researchers in fair learning have developed correlation or association-based measures such as…
Achieving Differential Privacy in Vertically Partitioned Multiparty Learning
Depeng Xu, Shuhan Yuan, Xintao Wu
Preserving differential privacy has been well studied under centralized setting. However, it's very challenging to preserve differential privacy under multiparty setting, especiall…
PC-Fairness: A Unified Framework for Measuring Causality-based Fairness
Yongkai Wu, Lu Zhang, Xintao Wu +1
A recent trend of fair machine learning is to define fairness as causality-based notions which concern the causal connection between protected attributes and decisions. However, on…
Insider Threat Detection via Hierarchical Neural Temporal Point Processes
Shuhan Yuan, Panpan Zheng, Xintao Wu +1
Insiders usually cause significant losses to organizations and are hard to detect. Currently, various approaches have been proposed to achieve insider threat detection based on ana…
Heterogeneous Gaussian Mechanism: Preserving Differential Privacy in Deep Learning with Provable Robustness
NhatHai Phan, Minh Vu, Yang Liu +4
In this paper, we propose a novel Heterogeneous Gaussian Mechanism (HGM) to preserve differential privacy in deep neural networks, with provable robustness against adversarial exam…