The privacy of EHR is at most concern and challenging as the records are shared and validated across multiple users and databases. In this proposed system the databases at local servers (independent hospitals) are co-aligned to form a local database network, integrated via multiple hospitals from distributed geographical locations. The proposed framework is subjected with a local integration unit (LIU) for extracting and mapping local attributes at LIU. The process is further associated with attribute extraction and attribute occurrence mapping. The process of attribute weight and occurrence mapping is subjected with local integration blockchain generation (LIBG). The LIBG blocks are framed with attribute mapping with respect to the attribute occurrence ratio. The objective is to generate a smarter and simplified privacy enhancer module at local server of blockchain to end-user security. The technique has secured an accuracy of 97.48% in attribute mapping with respect to occurrence.
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CRediT Author Statement
The authors confirm contribution to the paper as follows:
Conceptualization: Namrata Bathli and Shantaladevi Patil;
Methodology: Namrata Bathli;
Software: Shantaladevi Patil;
Data Curation: Namrata Bathli;
Writing- Original Draft Preparation: Namrata Bathli and Shantaladevi Patil;
Visualization: Shantaladevi Patil;
Investigation: Namrata Bathli and Shantaladevi Patil;
Supervision: Shantaladevi Patil;
Validation: Namrata Bathli;
Writing- Reviewing and Editing: Namrata Bathli and Shantaladevi Patil; All authors reviewed the results and approved the final version of the manuscript.
Acknowledgements
Author(s) thanks to Dr. Shantaladevi Patil for this research completion and support.
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Namrata Bathli
School of Computer Science and Engineering, REVA University, Bengaluru, Karnataka, India.
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Cite this article
Namrata Bathli and Shantaladevi Patil, “Design and Development of EHR Patterns in Local Blockchain Computing Layers for Privacy Enhancing Techniques”, Journal of Machine and Computing, vol.5, no.4, pp. 2103-2112, October 2025, doi: 10.53759/7669/jmc202505163.
