A Bio-Inspired Privacy Preservation Framework for Interactive Mobile Healthcare Applications in Cloud Environments

Doddi Srilatha; Niroj Kumar Pani; Sejal Mishra; A. Sree Lakshmi; Jyoti Kanjalkar; Ketan Anand

doi:10.3991/ijim.v20i13.62246

Authors

Doddi Srilatha Koneru Lakshmaiah Education Foundation, Bowrampet, Hyderabad, India https://orcid.org/0000-0002-0099-5277
Niroj Kumar Pani Indira Gandhi Institute of Technology, Sarang, Odisha, India https://orcid.org/0000-0001-8077-8661
Sejal Mishra Chandigarh University, Mohali, Punjab, India
A. Sree Lakshmi ICFAI Foundation for Higher Education, Hyderabad, Telangana, India https://orcid.org/0000-0002-3708-5293
Jyoti Kanjalkar Vishwakarma Institute of Technology, Pune, Maharashtra, India
Ketan Anand Sharda University, Greater Noida, Uttar Pradesh, India

DOI:

https://doi.org/10.3991/ijim.v20i13.62246

Keywords:

Data encryption, data restoration, data sanitization, Discrete Grey Wolf Optimizer, cloud storage

Abstract

The rapid adoption of interactive mobile healthcare applications and cloud-enabled medical services has significantly increased the volume of sensitive patient information stored and exchanged across distributed cloud environments. Mobile health (mHealth) systems frequently rely on cloud-based repositories to support real-time accessibility, remote monitoring, and intelligent healthcare analytics. However, the involvement of third-party platforms and remote data processing introduces serious concerns related to privacy preservation and unauthorized disclosure of medical records. To address these challenges, this study proposes a bio-inspired optimization framework based on the Discrete Grey Wolf Optimizer (DGWO) for effective optimized key generation in healthcare data sanitization processes. The proposed approach integrates perturbation-based privacy preservation with optimized key management to enhance secure data sharing in mobile cloud healthcare environments while maintaining data utility for analytical tasks. The framework is evaluated using four benchmark healthcare datasets under multiple performance measures, including privacy preservation rate, information entropy, resistance against inference attacks, and data utility retention. Experimental results demonstrate that the proposed DGWO-based sanitization mechanism outperforms existing perturbation approaches in achieving a balanced tradeoff between privacy and utility. The proposed model is particularly suitable for interactive mobile healthcare systems that require secure, efficient, and privacy-aware cloud data management.

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