Research on Revenue Transparency Mechanisms for Creator Platforms Based on Differential Privacy

The exponential growth of the creator economy has intensified demands for transparent revenue mechanisms while simultaneously raising critical privacy concerns. This paper proposes a novel differential privacy framework specifically designed for creator platform revenue transparency systems. Our approach addresses the fundamental tension between creators' information needs and privacy protection requirements through mathematically rigorous privacy guarantees. We develop specialized noise injection mechanisms for revenue data aggregation, implement dynamic privacy budget allocation strategies, and design utility preservation techniques that maintain statistical significance of revenue insights. Experimental evaluation demonstrates that our framework achieves substantial privacy protection while preserving 87.3% utility for revenue transparency reporting. The proposed system provides quantifiable privacy guarantees through -differential privacy with configurable privacy parameters ranging from 0.1 to 2.0, enabling platforms to balance transparency requirements with privacy constraints.