14 citations · 49 across the 14 of their papers we have counts for
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cs.CR2023★ 14 cited
Quantifying and Defending against Privacy Threats on Federated Knowledge Graph Embedding
Yuke Hu, Wei Liang, Ruofan Wu +5
Knowledge Graph Embedding (KGE) is a fundamental technique that extracts expressive representation from knowledge graph (KG) to facilitate diverse downstream tasks. The emerging fe…
cs.CR2020
Privacy Enhancement via Dummy Points in the Shuffle Model
Xiaochen Li, Weiran Liu, Hanwen Feng +4
The shuffle model is recently proposed to address the issue of severe utility loss in Local Differential Privacy (LDP) due to distributed data randomization.In the shuffle model, a…
cs.CR2020★ 3 cited
PGLP: Customizable and Rigorous Location Privacy through Policy Graph
Yang Cao, Yonghui Xiao, Shun Takagi +6
Location privacy has been extensively studied in the literature. However, existing location privacy models are either not rigorous or not customizable, which limits the trade-off b…