Privacy-Preserving Fair Benchmarking in Platform-Mediated Markets

Abstract

Digital platforms increasingly act as market intermediaries that establish competitive dynamics through the algorithmic curation of information. The design of benchmarking systems on such platforms raises pressing issues related to fairness, transparency, and the potential for market manipulation that have received limited academic attention. Against this backdrop, this paper examines the algorithmic fairness implications of peer group formation mechanisms employed by platform-mediated competitive intelligence systems. A comprehensive framework is presented through which fairness in privacy-preserving benchmarking systems can be evaluated, introducing new metrics accounting for both individual participant fairness and market-level equity considerations. The article indicates that conventional clustering methods for creating peer groups often lead to unfair competitive advantages that are systematically biased toward particular types of market participants, distorting market outcomes. This article presents algorithmic innovations that integrate fairness-sensitive constraints into privacy-preserving peer group creation while sustaining analytical utility and ε-differential privacy guarantees. Simulations using digital marketplace datasets illustrate a substantial reduction in group size bias with ε-DP guarantees maintained. The policy implications of these findings for platform regulation and antitrust oversight are examined, arguing for transparency in algorithmic governance and showcasing theoretical scenarios that illustrate the potential social benefits of equitable benchmarking, including improved market access for independent vendors. The framework provides a blueprint for responsible platform design, one that aligns commercial innovation with ethical equity and regulatory compliance in the digital economy.