PRIVACY-PRESERVING SECURITY MODEL FOR EARLY CANCER DIAGNOSIS, POPULATION-LEVEL EPIDEMIOLOGY, AND SECURE INTEGRATION INTO U.S. HEALTHCARE SYSTEMS

Abstract

The increasing use of data-intensive analytics for early cancer diagnosis and population-level epidemiology has heightened concerns regarding patient privacy, data security, and analytical reliability within healthcare systems. Early detection and cancer surveillance depend on large-scale integration of clinical, imaging, and epidemiological data across institutional boundaries, creating complex environments with elevated risks of disclosure, re-identification, and system vulnerability. This study investigates the effectiveness of a privacy-preserving security model for supporting early cancer diagnosis and population-level epidemiological analysis within regulated healthcare data environments in the United States. Using a quantitative, cross-sectional design, the study analyzed 120 oncology-related analytic units, including healthcare systems, cancer registries, and public health platforms, to assess the implementation of privacy controls, security architectures, and analytical performance outcomes. Results indicate a mean Privacy Control Index score of 68.4 (SD = 11.7), reflecting moderate to high adoption of privacy-preserving mechanisms with notable variability across institutions. Security architectures were more consistently implemented, with a mean Security Control Score of 72.9 (SD = 9.6), and were found to be a significant positive predictor of analytical performance. Analytical outputs remained stable under privacy constraints, with an overall performance stability score of 0.84 (SD = 0.06). The relationship between privacy strength and analytical performance was weak and non-linear (r = −0.21, p < .05), indicating that stronger privacy controls did not substantially degrade analytic utility. At the population level, secure aggregation mechanisms achieved high consistency, with a mean Aggregation Consistency Index of 88.1 (SD = 6.4), supporting reliable epidemiological analysis while limiting disclosure risk. Overall, the findings provide empirical evidence that privacy-preserving security models can be effectively integrated into cancer analytics systems, demonstrating that privacy, security, and analytical performance can function as complementary components within modern healthcare data infrastructures.