An Efficient Privacy-preserving Logistic Regression Scheme for Aging-in-place Systems

With the global demographic trend showing an increase in the elderly population, there is a pressing demand for innovative approaches to monitor and enhance the quality of life for this segment. In response to the growing need for advanced healthcare solutions for the aging population, this study presents an efficient privacy-preserving logistic regression scheme for aging-in-place to improve the safety and well-being of elderly individuals significantly. Furthermore, in light of increasing cybersecurity threats and the sensitivity of health data, the scheme introduces a novel zero-sum method, as well as matrix encryption. These measures are designed to secure users' health data and safeguard the logistic regression model's vital parameters against unauthorized access. The combination of predictive analytics and data security protocols offers a comprehensive solution to support elderly care, making significant strides toward ensuring the privacy and protection of personal health information. This work is pivotal in enhancing elderly care through innovative technology and robust data security.