Privacy-Preserving Percentile Visualization for Digital Marketplace Performance Metrics

Abstract

Digital marketplace platforms face increasing regulatory pressure to balance analytical utility with privacy protection when displaying vendor performance metrics. This article presents a comprehensive theoretical framework for privacy-aware visualization of percentile-based performance metrics that maintains analytical insight while protecting sensitive competitive intelligence and operational data. Our framework represents novel theoretical contributions addressing critical challenges in competitive marketplace environments through three core obfuscation techniques, percentile-range abstraction, endpoint approximation, and noise-calibrated interval sampling, integrated with differential privacy mechanisms to create visualizations that support informed decision-making without exposing underlying data distributions. We address critical limitations in traditional privacy-preserving approaches by introducing novel methods for handling duplicate values in performance datasets, a pervasive challenge in real-world marketplace metrics. Experimental validation through simulated performance datasets demonstrates that such techniques can substantially reduce information leakage while preserving analytical utility. This framework contributes both theoretical advances in privacy-preserving visualization design and practical implementation strategies applicable to e-commerce platforms, service marketplaces, enterprise monitoring systems, and regulated industries. Our work establishes new conceptual benchmarks for balancing transparency and confidentiality in performance monitoring systems while addressing compliance requirements under GDPR, CCPA, and emerging data protection frameworks.