Towards Split Learning-based Privacy-Preserving Record Linkage
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866929483193253888 |
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| author | Zervas, Michail Karakasidis, Alexandros |
| author_facet | Zervas, Michail Karakasidis, Alexandros |
| contents | Split Learning has been recently introduced to facilitate applications where user data privacy is a requirement. However, it has not been thoroughly studied in the context of Privacy-Preserving Record Linkage, a problem in which the same real-world entity should be identified among databases from different dataholders, but without disclosing any additional information. In this paper, we investigate the potentials of Split Learning for Privacy-Preserving Record Matching, by introducing a novel training method through the utilization of Reference Sets, which are publicly available data corpora, showcasing minimal matching impact against a traditional centralized SVM-based technique. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_01088 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Towards Split Learning-based Privacy-Preserving Record Linkage Zervas, Michail Karakasidis, Alexandros Cryptography and Security Databases Machine Learning Split Learning has been recently introduced to facilitate applications where user data privacy is a requirement. However, it has not been thoroughly studied in the context of Privacy-Preserving Record Linkage, a problem in which the same real-world entity should be identified among databases from different dataholders, but without disclosing any additional information. In this paper, we investigate the potentials of Split Learning for Privacy-Preserving Record Matching, by introducing a novel training method through the utilization of Reference Sets, which are publicly available data corpora, showcasing minimal matching impact against a traditional centralized SVM-based technique. |
| title | Towards Split Learning-based Privacy-Preserving Record Linkage |
| topic | Cryptography and Security Databases Machine Learning |
| url | https://arxiv.org/abs/2409.01088 |