4 papers
Defense against Poisoning Attacks under Shuffle-DP
Siyi Wang, Qiyao Luo, Yihua Hu +6
Differential Privacy (DP) has become the gold standard for protecting individual privacy in data analytics, and the shuffle-DP model has attracted significant attention from both a…
Acyclic Graph Pattern Counting under Local Differential Privacy
Yihua Hu, Kuncan Wang, Wei Dong
Graph pattern counting serves as a cornerstone of network analysis with extensive real-world applications. Its integration with local differential privacy (LDP) has gained growing…
N2E: A General Framework to Reduce Node-Differential Privacy to Edge-Differential Privacy for Graph Analytics
Yihua Hu, Hao Ding, Wei Dong
Differential privacy (DP) has been widely adopted to protect sensitive information in graph analytics. While edge-DP, which protects privacy at the edge level, has been extensively…
A General Framework for Per-record Differential Privacy
Xinghe Chen, Dajun Sun, Quanqing Xu +1
Differential Privacy (DP) is a widely adopted standard for privacy-preserving data analysis, but it assumes a uniform privacy budget across all records, limiting its applicability…