36 citations · 40 across the 48 of their papers we have counts for
4 papers · 1 filter
From Randomized Response to Randomized Index: Answering Subset Counting Queries with Local Differential Privacy
Qingqing Ye, Liantong Yu, Kai Huang +3
Local Differential Privacy (LDP) is the predominant privacy model for safeguarding individual data privacy. Existing perturbation mechanisms typically require perturbing the origin…
PrivDPR: Synthetic Graph Publishing with Deep PageRank under Differential Privacy
Sen Zhang, Haibo Hu, Qingqing Ye +1
The objective of privacy-preserving synthetic graph publishing is to safeguard individuals' privacy while retaining the utility of original data. Most existing methods focus on gra…
Trajectory Data Collection with Local Differential Privacy
Yuemin Zhang, Qingqing Ye, Rui Chen +2
Trajectory data collection is a common task with many applications in our daily lives. Analyzing trajectory data enables service providers to enhance their services, which ultimate…
TED: Towards Discovering Top-k Edge-Diversified Patterns in a Graph Database
Kai Huang, Haibo Hu, Qingqing Ye +3
With an exponentially growing number of graphs from disparate repositories, there is a strong need to analyze a graph database containing an extensive collection of small- or mediu…