Game-theoretic analysis of healthcare data protection framework
Shekhar M. Kumbhardrshekharwinofitlifestyle@gmail.comDepartment of Community MedicineKrishna Institute of Medical SciencesKrishna Vishwa Vidyapeeth (Deemed to be University)Karad, Maharashtra, 415539, IndiaView full profile → , Varun Kumar Sharmavarun1.sharma@niu.edu.inDepartment of Biotechnology and MicrobiologyNoida International UniversityGreater Noida, Uttar Pradesh, 201312, IndiaView full profile → , *Sulabha Narendra PatilCorresponding authorsulbha.patil@vit.eduDepartment of Engineering Science and HumanitiesVishwakarma Institute of TechnologyPune, Maharashtra, 411037, IndiaView full profile → , D. Anandhianandhid@maher.ac.inDepartment of BiochemistryMeenakshi Ammal Dental College & HospitalMeenakshi Academy of Higher Education & ResearchChennai, Tamil Nadu, 600095, IndiaView full profile → , P. Ramya Krishnapramyakrishna@outlook.comDepartment of Electronics & Communication EngineeringPragati Engineering CollegeKakinada, Andhra Pradesh, 533437, IndiaView full profile → , Abhishek Dixitabhishek1554@vardhaman.orgDepartment of Computer Science and Engineering (Artificial Intelligence & Machine Learning)Vardhaman College of EngineeringShamshabad, Hyderabad, Telangana, 501218, IndiaView full profile →
* Corresponding author · click or hover a name for details
- Received:
- 01 Mar 2026
- Published Online:
- 31 Oct 2026
- Article type:
- Research Article
- Language:
- EN
- Article no.:
- JIOS-2495
- Pages:
- 3693–3706
Abstract
Strategic attackers, insider and privacy–utility conflicts pose persisting problems to healthcare data protection frameworks. In this study we envision a Game-Theoretic Healthcare Data Protection Model (GHDPM) to make optimal defensive decisions in dynamic cyber-risk scenarios. The methodology models the interactions between the healthcare defenders and attackers as a non-cooperative Stackelberg security game where attacks, expected utility, privacy loss and defense costs are modeled. The best protection strategies are chosen using the concepts of nash equilibrium and payoff optimization. Analysis shows that an equilibrium-based defense strategy will decrease the probability of successful attack by 31.8%, the expected privacy loss by 27.4% with respect to static strategies while at the same time increasing security utility by 22.6%. The essence of the novelty is that adaptive strategic defense modeling takes place. The framework provides quantitative support for decision making in healthcare cybersecurity, showcasing the ability of game theoretic optimization to achieve resilient data protection of healthcare systems that is cost-effective and private.
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References
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