Privacy-Preserving Federated Analytics Across Multi-Employer Data Ecosystems: A Cross-Organizational Intelligence Architecture for Benchmark-Driven Decision Support in Enterprise HR and Benefits Platforms

Abstract

Providers of enterprise HR and benefits platforms at scale have thousands of employers as clients at once, and these data ecosystems of unprecedented analytic richness are multi-employer in nature. The benefits design, compensation benchmarking and investments in workforce planning value latent in these ecosystems is high: employers making benefits design choices, compensation benchmarking choices and investments in workforce planning would attract material benefits of cross-organizational comparisons that would reveal industry-specific performance distributions, region-specific dynamics in the labor market, and sector-calibrated risk norms. However, direct applications of multi-employer data to cross-organizational analytics inherently pose privacy, consent, and usage of data problems that have not enabled systematic exploitation of this value. The paper introduces the Federated Benchmark Intelligence Architecture (FBIA), which is a privacy-sensitive analytical system that enables one to generate cross-organizational insights using multi-employer data ecosystems without sharing data, processing data at a central point, or exposing data at the individual level across organizational boundaries. FBIA is composed of three technical components: a secure multi-party computation protocol to compute population-level statistical benchmarks without revealing individual employer raw data; a differentially-privacy mechanism to give formally-quantified privacy guarantees to the outputs of population-level statistical benchmarks; and a contextual calibration layer to tune the outputs of cross-organizational population-level statistical benchmarks to confounding variations in the composition of employer groups, industry sector and geographic region. The operationalization of FBIA benchmark quality uses four formally-defined measures: the Benchmark Representativeness Index (BRI), Privacy Budget Utilization Rate (PBUR), Calibration Accuracy Score (CAS), and Cross-Employer Signal-to-Noise Ratio (CESNR). Experiments on a synthetic 340-employer ecosystem of 2.1 million covered employees have shown that FBIA provides benchmark distributions within 3.8% mean absolute error of the true population parameters, with (epsilon, delta)-differential privacy providing at epsilon=1.2, or materially stronger privacy protection than data sharing methods without compromising benchmark utility sufficient to support employer decision-making. These results establish FBIA as a deployable framework for transforming multi-employer platform data into privacy-preserving competitive intelligence without violating the organizational data boundaries that clients expect.