175 citations · 313 across the 6 of their papers we have counts for
6 papers
DPMLBench: Holistic Evaluation of Differentially Private Machine Learning
Chengkun Wei, Minghu Zhao, Zhikun Zhang +5
Differential privacy (DP), as a rigorous mathematical definition quantifying privacy leakage, has become a well-accepted standard for privacy protection. Combined with powerful mac…
FACE-AUDITOR: Data Auditing in Facial Recognition Systems
Min Chen, Zhikun Zhang, Tianhao Wang +2
Few-shot-based facial recognition systems have gained increasing attention due to their scalability and ability to work with a few face images during the model deployment phase. Ho…
PrivGraph: Differentially Private Graph Data Publication by Exploiting Community Information
Quan Yuan, Zhikun Zhang, Linkang Du +3
Graph data is used in a wide range of applications, while analyzing graph data without protection is prone to privacy breach risks. To mitigate the privacy risks, we resort to the…
Finding MNEMON: Reviving Memories of Node Embeddings
Yun Shen, Yufei Han, Zhikun Zhang +5
Previous security research efforts orbiting around graphs have been exclusively focusing on either (de-)anonymizing the graphs or understanding the security and privacy issues of g…
Graph Unlearning
Min Chen, Zhikun Zhang, Tianhao Wang +3
Machine unlearning is a process of removing the impact of some training data from the machine learning (ML) models upon receiving removal requests. While straightforward and legiti…
When Machine Unlearning Jeopardizes Privacy
Min Chen, Zhikun Zhang, Tianhao Wang +3
The right to be forgotten states that a data owner has the right to erase their data from an entity storing it. In the context of machine learning (ML), the right to be forgotten r…