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2026 Vol.17, Issue 1 Preview Page

General Article

Privacy-preserving blockchain healthcare systems for healthy and sustainable smart buildings
Yogesh Kushwaha1*
,
Niranjan Lal2
,
Manisha Manjul3
1Research Scholar, Department of Computer Science and Engineering, SRM Institute of Science and Technology, Delhi-NCR Campus, Ghaziabad 201204, U.P., India
2Associate Professor, Department of Computer Science and Engineering, SRM Institute of Science and Technology, Delhi-NCR Campus, Ghaziabad 201204, U.P., India
3Associate Professor, Department of Computer Science and Engineering, G.B. Pant Engineering College, Delhi –110020, India
*Corresponding Author
31 March 2026. pp. 154-179
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Abstract

The swift digitization of Healthcare 4.0 smart building environments has intensified apprehensions about secure storage, privacy-preserving analytics, and responsible sharing of Electronic Health Records. Traditional deep learning systems handle plaintext data, so revealing sensitive information, whereas current blockchain-based solutions enhance traceability but do not facilitate computation in an encrypted environment. Likewise, the majority of homomorphic encryption methodologies concentrate on independent inference, lacking integrated consent enforcement or comprehensive system evaluation. A privacy-preserving Deep Learning-as-a-Service system that integrates CKKS-based homomorphic inference with permissioned blockchain governance is presented to overcome these issues. A BiLSTM model is utilized for longitudinal EHR modeling, whereas smart contracts facilitate consent validation and provide tamper-evident access recording inside a Healthcare 4.0 tiered architecture. In plaintext evaluation, the BiLSTM attained a precision of 0.882, a recall of 0.781, and an F1-score of 0.828, surpassing the GRU baseline by nearly 7 percent. Encrypted inference maintained predictive performance while elevating average latency from 71.3 ms to 720.0 ms per record, with the entire blockchain-integrated workflow averaging 760 ms. The system illustrates that secure, auditable, and confidentiality-preserving disease prediction is achievable with manageable computational costs, providing a unique advancement beyond standalone blockchain or homomorphic healthcare solutions.

Keywords
homomorphic encryption
CKKS
blockchain-based healthcare
privacy-preserving deep learning
BiLSTM
Healthcare 4.0 systems
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Information
  • Publisher :Sustainable Building Research Center (ERC) Innovative Durable Building and Infrastructure Research Center
  • Publisher(Ko) :건설구조물 내구성혁신 연구센터
  • Journal Title :International Journal of Sustainable Building Technology and Urban Development
  • Volume : 17
  • No :1
  • Pages :154-179
  • Received Date : 2026-01-26
  • Accepted Date : 2026-02-23
  • DOI :https://doi.org/10.22712/susb.20260010
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