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 embeddings. Research recently studied how to apply differential privacy to a number of graphs (and KG) analyses, but embedding methods have not been considered so far. This study moves a step toward filling such a gap, by proposing the Differential Private Knowledge Graph Embedding (DPKGE) framework.

DPKGE extends existing KG embedding methods (e.g., TransE, TransM, RESCAL, and DistMult) and processes KGs containing both confidential and unrestricted statements. The resulting embeddings protect the presence of any of the former statements in the embedding space using differential privacy. Our experiments identify the cases where DPKGE produces useful embeddings, by analyzing the training process and tasks executed on top of the resulting embeddings.

知识图 (KG) 嵌入方法是许多基于 KG 的数据挖掘任务的基础，例如链接预测和节点聚类。但是，图表可能包含有关人员或组织的机密信息，这些信息可能会通过嵌入泄露。最近的研究研究了如何将差分隐私应用于许多图（和 KG）分析，但迄今为止尚未考虑嵌入方法。这项研究通过提出差分私有知识图嵌入 (DPKGE) 框架，朝着填补这一空白迈出了一步。

DPKGE 扩展了现有的 KG 嵌入方法（例如，TransE、TransM、RESCAL 和 DistMult）并处理包含机密和非限制语句的 KG。生成的嵌入使用差分隐私保护嵌入空间中任何先前语句的存在。我们的实验通过分析训练过程和在生成的嵌入之上执行的任务来确定 DPKGE 产生有用嵌入的情况。