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International Journal of Machine Learning and Cybernetics
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02-03-2024 | Original Article

Survey and open problems in privacy-preserving knowledge graph: merging, query, representation, completion, and applications

Authors: Chaochao Chen, Fei Zheng, Jamie Cui, Yuwei Cao, Guanfeng Liu, Jia Wu, Jun Zhou

Published in: International Journal of Machine Learning and Cybernetics | Issue 8/2024

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Abstract

The article delves into the growing importance of Knowledge Graphs (KGs) for connecting diverse data types and supporting rich data services. It highlights the data isolation problem, where companies hold private KGs, limiting the effectiveness of artificial intelligence applications. The author discusses the need for privacy-preserving techniques such as collaborative learning and federated learning to address this issue. The main focus is on open problems in privacy-preserving KGs, including merging, querying, representing, and completing KGs securely. The article also showcases real-world applications like risk assessment, fraud detection, and recommender systems under KG isolation settings. It concludes with future research directions, emphasizing the balance between privacy and utility, data heterogeneity, and decentralized KGs.
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Metadata
Title
Survey and open problems in privacy-preserving knowledge graph: merging, query, representation, completion, and applications
Authors
Chaochao Chen
Fei Zheng
Jamie Cui
Yuwei Cao
Guanfeng Liu
Jia Wu
Jun Zhou
Publication date
02-03-2024
Publisher
Springer Berlin Heidelberg
Published in
International Journal of Machine Learning and Cybernetics / Issue 8/2024
Print ISSN: 1868-8071
Electronic ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-024-02106-6

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