Abstract:
The expanding success of cloud computing in the management of Electronic Health
Records (EHRs) also comes with loads of challenges, especially those that bothers on the preservation of users’ privacy, personal data integrity, and even reduction in
computational resource demand. Today, there is explosive growth in concerns for
security of data exchanged between edge node devices (users) and the cloud server.
Almost all the existing proposed methods for overcoming these privacy issues have
significant shortcomings such as, ineffectiveness or inefficiencies, as well as their lack of the lightweight property required by resource constrained devices used by users at the
edge of the network. This current research work is therefore motivated to develop a
lattice cryptography-based quantum attack-resistant security system for preserving
privacy and ensuring data integrity in health cloud big data. The lattice encryption
algorithm is used to encrypt medical records or data before uploading them on to the
storage servers. For every file uploaded to the cloud server, a decoy (or fake equivalent)
file is generated and stored in the decoy medical files repository that resides on a decoy
server (in the fog facility). This honeypot security paradigm is used here for
deceiving/luring potential attackers (unauthorized), to leaving trails behind each time
they make attempt to access secured medical records. This chemistry adds multiple
layers of security and satisfy requirements such as capacity, security and robustness for
secure medical data transmission in a fog-cloud computing environment. The proposed
health cloud data security solution was implemented using PHP/Python programming
language, on a Windows 10 Operating System running on a PC characterized by 8GB of
RAM, 2TB of Hard Disk, Intel Core i7. Comparative evaluation of the proposed solution
was then carried out using standard metrics such as time consumption/computation time, throughput, memory requirement, and bandwidth consumption. Results obtained reveals that the developed system performed better than existing ones in terms of having
robustness, requiring far lesser computation time, and the lightweight property
