TY - GEN
T1 - Enhancing Healthcare Data Confidentiality Through Fragmentation Techniques Within Cloud-Enabled Intelligent IoT Security and Privacy Frameworks
AU - Andreou, Andreas
AU - Mavromoustakis, Constandinos X.
AU - Markakis, Evangelos
AU - Bourdena, Athina
AU - Mastorakis, George
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
PY - 2025
Y1 - 2025
N2 - 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 F1-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.
AB - 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 F1-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.
KW - cloud repository
KW - data exchange
KW - encryption
KW - fragmentation
KW - healthcare
UR - https://www.scopus.com/pages/publications/105005936616
U2 - 10.1007/978-3-031-76459-2_27
DO - 10.1007/978-3-031-76459-2_27
M3 - Conference contribution
AN - SCOPUS:105005936616
SN - 9783031764585
T3 - Lecture Notes in Networks and Systems
SP - 291
EP - 300
BT - Distributed Computing and Artificial Intelligence, Special Sessions I, 21st International Conference
A2 - Mehmood, Rashid
A2 - Hernández, Guillermo
A2 - Praça, Isabel
A2 - Wikarek, Jaroslaw
A2 - Loukanova, Roussanka
A2 - Monteiro dos Reis, Arsénio
A2 - Skarmeta, Antonio
A2 - Lombardi, Eleonora
PB - Springer Science and Business Media Deutschland GmbH
T2 - 21st International Symposium on Distributed Computing and Artificial Intelligence, DCAI 2024
Y2 - 25 June 2024 through 27 June 2024
ER -