A framework for differentially-private knowledge graph embeddings |
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| Affiliation: | 1. Faculty of Management and Economics, Kunming University of Science and Technology, Kunming 650500, China;2. School of History and Administration, Yunnan Normal University, Kunming 650500, China;3. College of Electronic Information, Guangxi University for Nationalities, Nanning 530006, China;1. Key Laboratory of Systems and Control, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China;2. School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing, China;3. Department of Computer Science, Southwestern University of Finance and Economics, Chengdu, China;4. Institute of Artificial Intelligence, Beihang University, Beijing, China;1. National Engineering Research Center for E-Learning, Central China Normal University, Wuhan 430079, China;2. School of Education, Hubei University, No. 368 Youyi Road, Wuhan 430062, Hubei, China |
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| Abstract: | 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. |
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| Keywords: | Differential privacy Knowledge graph embeddings |
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