Privacy-Compliant User Characterization Framework in Enterprise Social Networks

In the context of enterprise social platforms, user modeling is essential for improving communication strategies, tailoring services, and enhancing engagement. However, traditional profiling approaches often raise concerns related to data privacy, transparency, and regulatory compliance. This study proposes a privacy-compliant user characterization framework that shifts from individual profiling to behavioral characterization, leveraging fuzzy logic. The framework was applied to a real-world case study involving a retail-sector customer of Beekeeper AG, a private enterprise social network, using a dataset of approximately 39,000 users.Using a combination of data anonymization, feature extension, and fuzzy clustering, users were segmented into interpretable behavioral groups based on app interaction patterns. The results demonstrate that the framework enables effective user modeling---capturing relevant usage typologies and behavioral trends---while ensuring alignment with GDPR principles, such as data minimization and user consent. Comparative evaluation against profiling-based baselines revealed that the characterization approach achieved similar levels of confidence in behavioral inference without relying on sensitive or identifiable attributes. This work highlights the potential of privacy-aware fuzzy methodologies to support ethical and effective personalization in enterprise platforms.