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Contribution Details

Type Journal Article
Scope Discipline-based scholarship
Title A framework for differentially-private knowledge graph embeddings
Organization Unit
Authors
  • Xiaolin Han
  • Daniele Dell’Aglio
  • Tobias Grubenmann
  • Reynold Cheng
  • Abraham Bernstein
Item Subtype Original Work
Refereed Yes
Status Published electronically before print/final form (Epub ahead of print)
Language
  • English
Journal Title Journal of Web Semantics
Publisher Elsevier
Geographical Reach international
ISSN 1570-8268
Page Range 100696
Date 2022
Abstract Text 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 towards 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.
Digital Object Identifier 10.1016/j.websem.2021.100696
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