In service-oriented industrial supply chains, enterprises collaborate to share information while maintaining strict privacy requirements that prohibit raw financial data exchange between corporate groups. We propose a novel Granular Fuzzy Rule-Based Model based on Density Peaks Clustering (DPC-GFRBM) for privacy-preserving revenue prediction in industrial supply chain networks. DPC-GFRBM performs local clustering within each enterprise group to identify revenue patterns, then aggregates these results through collaborative analysis without revealing individual data. A fuzzy rule-based model maps the granulated features to revenue predictions, balancing privacy protection with predictive accuracy. Experimental results on public and real-world enterprise revenue datasets show that DPC-GFRBM consistently outperforms traditional fuzzy rule-based methods, achieving over 20% improvements in the interval prediction balance index, while maintaining low computational overhead.