3 citations · 6 across the 3 of their papers we have counts for
8 papers
Breaking the Linear Error Barrier in Differentially Private Graph Distance Release
Chenglin Fan, Ping Li, Xiaoyun Li
Releasing all pairwise shortest path (APSP) distances between vertices on general graphs under weight Differential Privacy (DP) is known as a challenging task. In the previous atte…
Distances Release with Differential Privacy in Tree and Grid Graph
Chenglin Fan, Ping Li
Data about individuals may contain private and sensitive information. The differential privacy (DP) was proposed to address the problem of protecting the privacy of each individual…
Near-Optimal Correlation Clustering with Privacy
Vincent Cohen-Addad, Chenglin Fan, Silvio Lattanzi +4
Correlation clustering is a central problem in unsupervised learning, with applications spanning community detection, duplicate detection, automated labelling and many more. In the…
Linear Expected Complexity for Directional and Multiplicative Voronoi Diagrams
Chenglin Fan, Benjamin Raichel
While the standard unweighted Voronoi diagram in the plane has linear worst-case complexity, many of its natural generalizations do not. This paper considers two such previously st…
Generalized Metric Repair on Graphs
Chenglin Fan, Anna C. Gilbert, Benjamin Raichel +2
Many modern data analysis algorithms either assume or are considerably more efficient if the distances between the data points satisfy a metric. These algorithms include metric lea…
Skyline Diagram: Efficient Space Partitioning for Skyline Queries
Jinfei Liu, Juncheng Yang, Li Xiong +5
Skyline queries are important in many application domains. In this paper, we propose a novel structure Skyline Diagram, which given a set of points, partitions the plane into a set…