HUMAN-CENTERED PRIVACY-PRESERVING ANALYTICS FOR DIGITAL MARKETPLACE PLATFORMS

Abstract

The proliferatio of digital marketplace platforms has created unprecedented demand for competitive analytics while simultaneously raising critical privacy concerns for vendors and platform operators. This comprehensive framework addresses the design of privacy-preserving analytics dashboards that balance information utility with robust privacy protection through intelligent obfuscation techniques. The convergence of human-computer interaction principles, differential privacy mechanisms, and cognitive psychology addresses the fundamental challenge of maintaining analytical accuracy while protecting sensitive competitive data. Through theoretical analysis and proposed evaluation approaches involving marketplace participants and business analysts, this framework suggests that well-designed obfuscation interfaces could paradoxically enhance decision-making quality by reducing information overload and focusing attention on statistically significant patterns. The framework integrates progressive disclosure mechanisms, adaptive privacy budgets, uncertainty visualization techniques, and human-AI collaboration models to create trustworthy analytics systems. Research suggests that enterprise users may demonstrate substantial distrust of automated privacy settings, highlighting the critical need for transparent, user-centric privacy controls. Novel interface design principles could enable users to maintain high analytical accuracy while working with privacy-preserved data, provided that systems clearly communicate privacy tradeoffs and incorporate interactive feedback mechanisms. The framework advances privacy-aware user experience design, competitive intelligence systems, and regulatory compliance interfaces with direct applications to digital marketplace platforms, enterprise analytics, and broader privacy-preserving data ecosystems.