Privacy-Preserving Competitive Intelligence: A Differential Privacy Framework For Digital Marketplace Benchmarking

Abstract

Large volumes of performance data are generated in digital marketplace platforms, enabling vendors and platform operators to make strategic decisions. However, traditional benchmarking faces significant privacy challenges due to aggregation over competing market participants with sensitive performance metrics. This paper presents a comprehensive differential privacy framework for peer-group benchmarking in digital marketplace ecosystems. The framework proposes a multi-layered privacy preservation mechanism that maintains statistical utility while protecting individual participant performance data through three core innovations: a categorical peer-group formation algorithm clustering similar market participants based on offering category, business model, and transaction volume tier; an accuracy-preserving noise injection mechanism calibrated to maintain epsilon-differential privacy while constraining accuracy loss within acceptable thresholds; and a user-interface abstraction visualizing relative performance without revealing individual data points. Theoretical validation using simulated marketplace datasets suggests the framework could achieve minimal root-mean-square error across key performance indicators, potentially preserving the ability of vendors to gauge market position while maintaining formal privacy guarantees. The proposed system bridges a long-standing gap between competitive transparency and protection of proprietary data in platform-mediated markets and lays theoretical foundations for privacy-preserving competitive intelligence systems while demonstrating conceptual implementation strategies for large-scale digital platforms. This article extends beyond traditional differential privacy applications by developing domain-specific optimizations for categorical data clustering and performance metric obfuscation, with wide-ranging implications for regulatory compliance frameworks, platform governance models, and broader adoption of privacy-preserving analytics in digital ecosystems.