A framework for differentially-private knowledge graph embeddings
(2021)
Journal Article
Han, X., Dell’Aglio, D., Grubenmann, T., Cheng, R., & Bernstein, A. (2022). A framework for differentially-private knowledge graph embeddings. Journal of Web Semantics, 72, Article 100696. https://doi.org/10.1016/j.websem.2021.100696
Knowledge graph (KG) embedding methods are at the basis of many KG-based data mining tasks, such as link prediction and node clustering. However, graphs may contain confidential information about people or organizations, which may be leaked via embed... Read More about A framework for differentially-private knowledge graph embeddings.