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Privacy-preserving logistic regression prediction over vertically partitioned data for AiP system

DOIResolve DOI: https://doi.org/10.1109/GLOBECOM59602.2025.11431817
AuthorSearch for: 1; Search for: 1; Search for: 2; Search for: 3ORCID identifier: https://orcid.org/0000-0002-4045-8687
Affiliation
  1. University of New Brunswick
  2. Queen's University
  3. National Research Council Canada. Digital Technologies
FunderSearch for: National Research Council Canada
FormatText, Article
ConferenceGLOBECOM 2025 - 2025 IEEE Global Communications Conference, December 8-12, 2025, Taipei, Taiwan
Subjectprivacy-preserving; aging in place; LR prediction; vertically partitioned database
Abstract
Aging in Place (AiP) programs enable elderly individuals to live independently and comfortably within their homes and communities by utilizing technological innovations such as smart homes and remote healthcare monitoring. In practical AiP scenarios, data necessary for accurate health predictions are typically vertically partitioned across multiple medical institutions, raising significant privacy concerns during data integration and analysis. To address this challenge, we propose an efficient and privacy-preserving logistic regression (LR) prediction scheme tailored explicitly for vertically partitioned AiP data. Our scheme effectively combines the computational efficiency of Trusted Execution Environments (TEE) under the honest-but-curious model with cryptographic security based on the Matrix Diffie-Hellman (MDDH) assumption. Security analysis confirms that our approach provides privacy protections against honest-but-curious adversaries. Extensive experimental evaluations demonstrate that our proposed scheme achieves computational efficiency, privacy protection, and practical scalability for real-world AiP implementations.
Date published
PublisherInstitute of Electrical and Electronics Engineers
In
  • GLOBECOM 2025 - 2025 IEEE Global Communications Conference, 11431817 (19 March 2026): 2276–2281.
LanguageEnglish
Peer reviewedYes
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Record identifier1212b8cf-3ddf-4e32-9bab-7593429fb261
Record created2026-04-16
Record modified2026-06-08

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