Scholarlink

In the context of the Internet of Things (IoT) era, securing and privatizing IoT-enabled healthcare systems present significant challenges, particularly in ensuring the confidentiality, integrity, and availability of health data exchange. This study explores data fragmentation and employs polynomial and Newton-Gregory’s divided difference interpolation techniques for encrypting sensitive health information, such as patient IDs, to enhance data security and utility. The research aims to improve data integrity and ensure end-user availability by fragmenting data. The performance of this methodology is thoroughly evaluated against modern techniques, showing notable superiority in precision, recall, and \(F_{1}\) -score across different correlation index values. Moreover, the study’s analysis of time complexity for overhead tasks highlights its efficiency compared to existing technologies. By emphasizing the need for collective efforts in addressing security and privacy concerns, this research contributes to building trust and encouraging the adoption of sophisticated healthcare technologies, paving the way for a secure, data-driven healthcare future.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Enhancing Healthcare Data Confidentiality Through Fragmentation Techniques Within Cloud-Enabled Intelligent IoT Security and Privacy Frameworks

  • 会议论文

  • 发表日期：

    2025年

查看全文
  • Andreas Andreou,
  • Constandinos X. Mavromoustakis,
  • Evangelos Markakis,
  • Athina Bourdena,
  • George Mastorakis

摘要

In the context of the Internet of Things (IoT) era, securing and privatizing IoT-enabled healthcare systems present significant challenges, particularly in ensuring the confidentiality, integrity, and availability of health data exchange. This study explores data fragmentation and employs polynomial and Newton-Gregory’s divided difference interpolation techniques for encrypting sensitive health information, such as patient IDs, to enhance data security and utility. The research aims to improve data integrity and ensure end-user availability by fragmenting data. The performance of this methodology is thoroughly evaluated against modern techniques, showing notable superiority in precision, recall, and \(F_{1}\) -score across different correlation index values. Moreover, the study’s analysis of time complexity for overhead tasks highlights its efficiency compared to existing technologies. By emphasizing the need for collective efforts in addressing security and privacy concerns, this research contributes to building trust and encouraging the adoption of sophisticated healthcare technologies, paving the way for a secure, data-driven healthcare future.