18 citations · 26 across the 6 of their papers we have counts for
6 papers
A Differentially Private Framework for Deep Learning with Convexified Loss Functions
Zhigang Lu, Hassan Jameel Asghar, Mohamed Ali Kaafar +2
Differential privacy (DP) has been applied in deep learning for preserving privacy of the underlying training sets. Existing DP practice falls into three categories - objective per…
TableGAN-MCA: Evaluating Membership Collisions of GAN-Synthesized Tabular Data Releasing
Aoting Hu, Renjie Xie, Zhigang Lu +2
Generative Adversarial Networks (GAN)-synthesized table publishing lets people privately learn insights without access to the private table. However, existing studies on Membership…
Protect Edge Privacy in Path Publishing with Differential Privacy
Zhigang Lu, Hong Shen
Paths in a given network are a generalised form of time-serial chains in many real-world applications, such as trajectories and Internet flows. Differentially private trajectory pu…
A Security-assured Accuracy-maximised Privacy Preserving Collaborative Filtering Recommendation Algorithm
Zhigang Lu, Hong Shen
The neighbourhood-based Collaborative Filtering is a widely used method in recommender systems. However, the risks of revealing customers' privacy during the process of filtering h…
An Accuracy-Assured Privacy-Preserving Recommender System for Internet Commerce
Zhigang Lu, Hong Shen
Recommender systems, tool for predicting users' potential preferences by computing history data and users' interests, show an increasing importance in various Internet applications…
A Faster Algorithm to Build New Users Similarity List in Neighbourhood-based Collaborative Filtering
Zhigang Lu, Hong Shen
Neighbourhood-based Collaborative Filtering (CF) has been applied in the industry for several decades, because of the easy implementation and high recommendation accuracy. As the c…