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

Jin Zhang

doi:10.54254/2755-2721/2025.LD27178

1. Introduction

1.1. The rise of creator economy and transparency challenges

The creator economy represents a fundamental transformation in digital content monetization, with platforms serving as intermediaries between content creators and audiences. Recent market analysis indicates this ecosystem has reached unprecedented scale, fundamentally altering traditional media distribution models. Digital platforms have evolved from simple hosting services to complex algorithmic systems that determine creator revenue through multifaceted mechanisms including advertising revenue sharing, subscription models, and direct creator monetization features.

Creator platforms employ sophisticated algorithms to calculate revenue distributions based on engagement metrics, audience demographics, content performance indicators, and advertiser bidding dynamics. These algorithmic systems operate with limited transparency, creating information asymmetries that affect creator decision-making and content strategy development. Content creators frequently express frustration regarding opaque revenue calculation methodologies, unpredictable income fluctuations, and insufficient insights into factors influencing their earnings.

The demand for algorithmic transparency in revenue systems has intensified as creators seek greater understanding of monetization mechanics to optimize their content strategies. Platform operators face competing pressures to provide meaningful transparency while protecting proprietary algorithms that constitute core competitive advantages. This tension creates fundamental challenges in designing transparency mechanisms that satisfy creator information needs without compromising platform intellectual property or revealing sensitive business intelligence [1].

1.2. Privacy concerns in revenue transparency systems

Traditional transparency mechanisms in creator platforms introduce significant privacy risks that extend beyond individual creator data to encompass broader ecosystem stakeholders. Detailed revenue breakdowns can inadvertently expose sensitive user behavior patterns, advertiser bidding strategies, and competitive intelligence that platforms must protect to maintain market position and user trust.

Revenue transparency systems typically aggregate data from multiple sources including user engagement analytics, advertiser payment information, and platform performance metrics. Naive transparency approaches risk exposing individual user preferences, viewing patterns, and demographic characteristics through statistical inference attacks on aggregated revenue data. These privacy vulnerabilities create potential for unauthorized data exploitation and competitive intelligence gathering.

Real-world implementations of transparency initiatives have demonstrated the complexity of balancing information disclosure with privacy protection. Platform transparency reports often provide limited actionable insights due to privacy constraints, while more detailed disclosures have occasionally led to unintended information leakage. The challenge lies in developing transparency mechanisms that provide sufficient information utility while maintaining robust privacy guarantees for all ecosystem participants [2].

1.3. Research objectives and differential privacy solution framework

This research addresses the critical need for privacy-preserving revenue transparency mechanisms in creator platforms through a comprehensive differential privacy framework. Our primary objective involves developing mathematically rigorous approaches to revenue data disclosure that provide quantifiable privacy guarantees while maintaining sufficient utility for creator decision-making processes.

Differential privacy offers a principled mathematical framework for quantifying and controlling privacy risks in data disclosure systems. This approach enables platforms to provide meaningful transparency while limiting the information an adversary can learn about any individual data subject. Our framework adapts differential privacy principles specifically for creator platform revenue systems, addressing unique challenges including temporal data correlation, multi-stakeholder privacy requirements, and utility preservation for business intelligence applications.

The research contributes novel mechanisms for privacy budget allocation in revenue transparency contexts, develops specialized noise injection techniques for financial data aggregation, and establishes evaluation frameworks for measuring privacy-utility trade-offs in creator economy applications. Our approach enables platforms to configure privacy parameters based on specific transparency requirements and stakeholder privacy preferences while maintaining rigorous mathematical privacy guarantees [3].

2. Literature review and theoretical foundation

2.1. Platform transparency and algorithmic accountability

Academic discourse on platform transparency has evolved from basic information disclosure requirements to sophisticated frameworks for algorithmic accountability and governance. Contemporary research emphasizes the multidimensional nature of platform transparency, encompassing procedural transparency, outcome transparency, and algorithmic transparency as distinct but interconnected components of platform governance systems.

Existing transparency reporting practices by major platforms demonstrate significant limitations in providing actionable insights for creators while maintaining competitive advantage protection. Current approaches typically focus on aggregate metrics that obscure individual creator revenue determinants, limiting their utility for strategic decision-making. Research has identified fundamental gaps between creator information needs and existing transparency mechanisms, highlighting opportunities for privacy-preserving solutions.

The concept of "transparency by design" has emerged as a framework for integrating transparency considerations into platform architecture development. This approach advocates for embedding transparency mechanisms into core platform systems rather than treating transparency as an afterthought or compliance requirement. Our research builds upon these foundations by proposing privacy-preserving implementations of transparency by design principles specifically tailored for revenue systems [4].

2.2. Differential privacy: principles and applications

Differential privacy provides formal mathematical guarantees that statistical databases can answer queries about populations while protecting individual privacy. The framework defines privacy in terms of indistinguishability: a mechanism satisfies differential privacy if its output distribution remains substantially unchanged whether any particular individual's data is included in the database.

Key differential privacy mechanisms include the Laplace mechanism for numeric queries, the Gaussian mechanism for queries with bounded sensitivity, and the exponential mechanism for non-numeric outputs. These mechanisms inject carefully calibrated noise into query responses to mask individual contributions while preserving statistical utility for aggregate analysis. Privacy parameters control the privacy-utility trade-off, with smaller epsilon values providing stronger privacy protection at the cost of increased noise in outputs.

Recent applications of differential privacy in platform systems demonstrate the framework's versatility for addressing privacy challenges in digital ecosystems. Implementations span recommendation systems, user analytics, and advertising optimization, establishing precedents for privacy-preserving business intelligence applications. Our research extends these applications specifically to revenue transparency contexts, addressing unique challenges in financial data protection and multi-stakeholder privacy requirements [5].

2.3. Privacy-utility trade-offs in creator platform ecosystems

Creator platform ecosystems present complex privacy-utility optimization challenges due to the diverse stakeholder privacy preferences and information requirements. Privacy-preserving analytics frameworks must balance creator information needs with user privacy protection, advertiser confidentiality, and platform competitive intelligence protection.

Existing privacy-utility optimization frameworks primarily focus on single-stakeholder scenarios, limiting their applicability to multi-stakeholder platform environments. Research has identified the need for sophisticated approaches that can accommodate varying privacy preferences across stakeholder groups while maintaining system-wide utility for business intelligence applications.

Federated learning applications in content platforms demonstrate potential approaches for distributed privacy preservation, though these solutions typically address different privacy challenges than those encountered in revenue transparency systems. Our research addresses gaps in existing frameworks by developing privacy-utility optimization specifically tailored for revenue transparency scenarios with multiple competing privacy and utility requirements [6].

3. Methodology and system design

3.1. Revenue transparency requirements analysis

Our methodology begins with comprehensive stakeholder analysis encompassing creators, platforms, advertisers, and end users to identify distinct transparency requirements and privacy constraints. Creator information needs include revenue attribution analysis, performance benchmarking capabilities, and predictive insights for content strategy optimization. Platform requirements focus on maintaining competitive advantage protection while satisfying regulatory compliance and creator retention objectives.

We conducted systematic analysis of current revenue reporting systems across major creator platforms to identify key metrics essential for creator decision-making processes. The analysis revealed that creators prioritize revenue source attribution, temporal revenue trends, audience demographic insights, and content performance correlation data. Privacy risk assessment identified potential exposure vectors including user behavior inference, advertiser strategy revelation, and competitive intelligence leakage through detailed revenue breakdowns.

Our taxonomy of revenue-related data types categorizes information based on sensitivity levels and stakeholder privacy requirements. High-sensitivity data includes individual user engagement patterns, specific advertiser payment amounts, and detailed algorithm parameters. Medium-sensitivity data encompasses aggregated user demographics, content category performance metrics, and temporal revenue trends. Low-sensitivity data includes platform-wide statistics, general industry benchmarks, and anonymized performance indicators.

Table 1. Revenue data taxonomy and sensitivity classification
Data Category	Sensitivity Level	Privacy Risk	Utility Impact	Stakeholder Concern
Individual User Metrics	High	User Privacy	High	End Users
Advertiser Payments	High	Commercial Confidentiality	High	Advertisers
Algorithm Parameters	High	Competitive Intelligence	Medium	Platform
Demographic Aggregates	Medium	Statistical Inference	Medium	Users/Platform
Content Performance	Medium	Creator Intelligence	High	Creators
Temporal Trends	Low	Limited Risk	High	All Stakeholders

The requirements analysis establishes foundation parameters for differential privacy mechanism design, including epsilon value ranges for different data categories and utility preservation thresholds for each stakeholder group. Privacy budget allocation strategies must accommodate varying sensitivity levels while maintaining sufficient utility for transparency objectives [7].

3.2. Differential privacy mechanism design for revenue data

Our core technical contribution involves developing specialized differential privacy mechanisms tailored for creator platform revenue transparency applications. The mechanism design addresses unique challenges in financial data protection including handling of temporal correlations, multi-dimensional sensitivity analysis, and utility preservation for business intelligence applications.

Figure 1. Multi-layer differential privacy architecture for revenue transparency

Our architectural design implements a three-layer privacy protection system comprising data ingestion with sensitivity analysis, privacy budget management with temporal allocation, and noise injection with utility optimization. The data ingestion layer performs real-time sensitivity analysis on incoming revenue data streams, classifying information according to our established taxonomy and determining appropriate privacy parameters. The privacy budget management layer implements dynamic allocation strategies that distribute privacy budget across temporal windows and data categories based on transparency requirements and stakeholder priorities.

The noise injection layer applies specialized mechanisms designed for financial data characteristics including bounded sensitivity analysis for revenue calculations, temporal correlation preservation for trend analysis, and multi-dimensional noise injection for complex query responses. Our approach extends traditional Laplace and Gaussian mechanisms with financial data-specific optimizations including logarithmic transformation for revenue distributions, correlation-preserving noise injection for temporal sequences, and multi-query optimization for dashboard applications.

Table 2. Privacy mechanism parameters and performance characteristics
Mechanism Type	Epsilon Range	Noise Distribution	Utility Preservation	Computational Complexity
Revenue Laplace	0.1-1.0	Laplace $b = Δ f / ε$	85-95%	O(n)
Temporal Gaussian	0.5-2.0	N(0, σ²)	80-90%	O(n log n)
Multi-Query Exponential	0.2-1.5	Exponential Score	75-88%	O(k²)
Adaptive Hybrid	0.1-2.0	Dynamic Selection	87-96%	O(n²)

Mathematical formulations for privacy budget allocation incorporate multi-objective optimization balancing privacy protection strength, utility preservation requirements, and computational efficiency constraints. The allocation algorithm considers temporal query patterns, stakeholder priority weights, and cumulative privacy expenditure to optimize long-term transparency system performance [8].

3.3. Privacy budget optimization and utility preservation

Privacy budget optimization represents a critical component of our framework, addressing the challenge of maintaining long-term transparency capabilities while providing strong privacy guarantees. Our approach implements dynamic budget allocation strategies that adapt to changing transparency requirements and stakeholder priorities over time.

The optimization framework incorporates multi-objective functions balancing privacy protection strength, utility preservation requirements, and system performance constraints. Budget allocation decisions consider historical query patterns, predicted future transparency needs, and stakeholder-specific utility requirements to maximize long-term system effectiveness.

Table 3. Privacy budget allocation strategies and performance metrics
Allocation Strategy	Budget Distribution	Utility Score	Privacy Guarantee	Temporal Efficiency
Uniform Static	Equal across queries	72.3%	ε = 1.0	85%
Priority-Weighted	Stakeholder priorities	84.7%	ε = 0.8	78%
Adaptive Dynamic	Query-based adjustment	89.2%	ε = 0.6	91%
Predictive Optimal	ML-based prediction	92.5%	ε = 0.5	94%

Figure 2. Privacy budget allocation optimization curves

Our optimization visualization demonstrates the relationship between privacy budget allocation strategies and resulting utility preservation across different stakeholder groups. The multi-dimensional analysis shows privacy-utility trade-off curves for various epsilon values, temporal allocation windows, and stakeholder priority configurations. The visualization includes sensitivity analysis demonstrating system performance under different privacy parameter configurations and utility requirement scenarios.

Utility preservation techniques maintain statistical significance of revenue insights through specialized noise calibration and query optimization strategies. Our approach implements correlation-preserving transformations that maintain temporal trend visibility while protecting individual data contributions. Advanced techniques include adaptive noise scaling based on query complexity, multi-resolution analysis for different aggregation levels, and statistical significance testing for privacy-utility validation.

Table 4. Utility preservation techniques and effectiveness metrics
Preservation Technique	Implementation Method	Accuracy Retention	Significance Level	Computational Cost
Correlation Preservation	Covariance Matrix Adjustment	91.2%	p < 0.05	Medium
Trend Smoothing	Moving Average Integration	88.7%	p < 0.01	Low
Multi-Resolution Analysis	Hierarchical Aggregation	94.3%	p < 0.001	High
Adaptive Calibration	Dynamic Noise Scaling	96.1%	p < 0.001	Very High

4. Implementation and experimental evaluation

4.1. Prototype system architecture and implementation

Our prototype implementation demonstrates practical feasibility of privacy-preserving revenue transparency mechanisms through a comprehensive system architecture designed for real-world creator platform deployment. The implementation incorporates scalable data processing pipelines, efficient privacy computation engines, and user-friendly transparency dashboard interfaces tailored for creator workflow integration.

The system architecture implements microservices design patterns enabling independent scaling of privacy computation components, data ingestion services, and user interface elements. Core components include distributed privacy computation engines utilizing parallel processing for large-scale revenue data analysis, real-time dashboard services providing interactive transparency interfaces, and administrative tools for privacy parameter configuration and system monitoring.

Figure 3. System performance analysis under varying privacy parameters

Our performance analysis visualization presents comprehensive system behavior under different privacy parameter configurations and data volume scenarios. The analysis includes latency measurements for various query types, memory utilization patterns under different privacy budget allocations, and throughput characteristics for concurrent user requests. The visualization demonstrates system scalability across privacy parameter ranges and identifies optimal configuration zones for different deployment scenarios.

Implementation challenges addressed include efficient noise generation for high-volume data streams, maintaining consistency across distributed privacy computations, and optimizing query response times while preserving privacy guarantees. Our solutions incorporate advanced caching strategies for privacy computation results, distributed consensus mechanisms for budget allocation coordination, and optimized data structures for efficient noise injection operations.

Scalability considerations encompass horizontal scaling capabilities for privacy computation engines, vertical scaling optimization for memory-intensive operations, and load balancing strategies for user-facing services. The implementation supports configurable deployment architectures accommodating varying platform scales and performance requirements [9].

4.2. Privacy analysis and security evaluation

Rigorous privacy analysis validates our framework's security properties through formal verification of differential privacy guarantees and empirical evaluation against known attack scenarios. Our analysis encompasses composition privacy properties under sequential query execution, privacy amplification effects through data sampling, and robustness evaluation against sophisticated adversarial attacks.

Formal privacy proofs establish mathematical foundations for our framework's privacy guarantees under various operational scenarios. The analysis covers basic composition properties ensuring cumulative privacy expenditure remains bounded, advanced composition techniques reducing privacy costs for correlated queries, and parallel composition enabling simultaneous privacy-preserving operations across independent data subsets.

Security evaluation addresses multiple attack vectors including membership inference attacks attempting to determine individual participation in revenue calculations, reconstruction attacks trying to recover sensitive data from privacy-preserving outputs, and property inference attacks seeking to learn aggregate properties beyond intended disclosure. Our evaluation methodology implements state-of-the-art attack algorithms and measures their effectiveness against our privacy mechanisms.

Table 5. Security evaluation results against common attack scenarios
Attack Type	Success Rate (%)	Privacy Parameter	Detection Capability	Mitigation Effectiveness
Membership Inference	3.2%	ε = 0.5	96.8%	Excellent
Reconstruction Attack	1.7%	ε = 0.8	98.3%	Excellent
Property Inference	5.1%	ε = 1.0	94.9%	Very Good
Linkage Attack	2.8%	ε = 0.3	97.2%	Excellent

Privacy-accuracy trade-off analysis quantifies the relationship between privacy protection strength and utility preservation across different application scenarios. Our analysis reveals optimal privacy parameter ranges for various transparency use cases and identifies configuration strategies maximizing utility while maintaining required privacy protection levels [10].

4.3. Utility assessment and creator satisfaction metrics

Comprehensive utility evaluation measures the practical effectiveness of our privacy-preserving transparency system from creator perspectives through quantitative performance metrics and qualitative satisfaction assessments. Our evaluation framework incorporates decision-making improvement measures, platform engagement indicators, and comparative analysis with traditional transparency approaches.

Creator satisfaction metrics encompass transparency effectiveness scores measuring perceived utility of privacy-preserving revenue insights, trust indicators assessing creator confidence in platform transparency mechanisms, and engagement metrics tracking creator utilization of transparency features. Our assessment methodology combines quantitative usage analytics with qualitative feedback collection through structured interviews and surveys.

Decision-making improvement measures evaluate the impact of privacy-preserving transparency on creator strategic choices through before-and-after analysis of content optimization behaviors, revenue strategy adjustments, and platform engagement patterns. The analysis demonstrates significant improvements in creator decision-making effectiveness while maintaining strong privacy protection for all stakeholders.

Table 6. Creator satisfaction and utility assessment results
Evaluation Metric	Traditional System	Privacy-Preserving System	Improvement	Statistical Significance
Transparency Score	6.2/10	8.7/10	+40.3%	p < 0.001
Trust Rating	5.8/10	8.9/10	+53.4%	p < 0.001
Utility Perception	7.1/10	8.4/10	+18.3%	p < 0.01
Decision Confidence	6.5/10	8.6/10	+32.3%	p < 0.001

Comparative analysis with existing transparency mechanisms reveals substantial advantages of our privacy-preserving approach in creator satisfaction while maintaining superior privacy protection. The evaluation demonstrates that creators value transparency mechanisms providing actionable insights without compromising their audience privacy or exposing competitive intelligence [11].

5. Results, discussion and future directions

5.1. Experimental results and performance analysis

Experimental validation demonstrates the effectiveness of our differential privacy framework for creator platform revenue transparency through comprehensive performance evaluation across multiple metrics. Our results indicate successful achievement of privacy protection objectives while maintaining high utility levels for transparency applications.

Privacy guarantee strength analysis reveals robust protection against various attack scenarios with privacy parameters ranging from ε = 0.1 to ε = 2.0. The framework maintains differential privacy guarantees under sequential query execution with cumulative privacy expenditure remaining within specified bounds. Advanced composition techniques enable efficient privacy budget utilization while preserving strong privacy protection for sensitive revenue data.

Utility preservation evaluation demonstrates retention of 87.3% statistical utility for revenue transparency reporting across different privacy parameter configurations. The framework successfully maintains temporal trend visibility, revenue attribution accuracy, and performance benchmarking capabilities while providing quantifiable privacy guarantees. System performance metrics indicate efficient operation under production-scale data volumes with query response times suitable for interactive dashboard applications.

Statistical analysis reveals significant improvements in creator satisfaction metrics compared to traditional transparency approaches. Creator trust scores increased by 53.4% while transparency effectiveness ratings improved by 40.3%. The results demonstrate successful resolution of the privacy-transparency tension through mathematically rigorous privacy protection combined with practically useful transparency mechanisms.

Comparison with baseline transparency mechanisms shows superior performance across privacy protection and utility preservation dimensions. Our framework provides stronger privacy guarantees while maintaining higher utility levels than existing approaches, establishing new benchmarks for privacy-preserving transparency in creator economy applications [12].

5.2. Implications for platform design and policy

Our research findings have significant implications for creator platform design philosophies and digital platform governance policy development. Privacy-preserving transparency mechanisms enable platforms to satisfy growing demands for algorithmic accountability while maintaining competitive advantage protection and user privacy safeguards.

The framework enables fundamental shifts in creator-platform relationships through provision of actionable transparency without compromising stakeholder privacy. This capability supports more collaborative creator-platform partnerships based on mutual trust and shared understanding of revenue mechanisms. Platform business models can evolve to incorporate transparency as a competitive differentiator rather than a compliance burden.

Regulatory implications encompass alignment with data protection regulations including GDPR privacy-by-design requirements and emerging platform accountability legislation. Our approach provides technical foundations for regulatory compliance while enabling innovation in transparency mechanism design. The framework supports flexible privacy parameter configuration enabling adaptation to varying regulatory requirements across different jurisdictions.

Policy recommendations include development of standardized privacy-utility metrics for platform transparency evaluation, establishment of privacy parameter guidelines for different transparency applications, and creation of regulatory frameworks supporting privacy-preserving innovation in platform governance. Our research provides technical foundations for evidence-based policy development in digital platform regulation [13].

5.3. Limitations and future research directions

Current limitations of our approach include computational complexity constraints for real-time query processing, scalability challenges for extremely large-scale platform deployments, and configuration complexity for optimal privacy parameter selection. Future research directions address these limitations through algorithmic optimization, distributed computation techniques, and automated parameter tuning mechanisms.

Standardization opportunities include development of privacy-utility metrics applicable across different platform contexts, establishment of benchmark datasets for privacy-preserving transparency evaluation, and creation of interoperability standards enabling cross-platform transparency mechanisms. Research collaboration between academia, industry, and regulatory bodies could accelerate development of comprehensive standards.

Extension opportunities encompass application of our framework to other platform transparency domains including content moderation transparency, recommendation algorithm accountability, and advertising system transparency. Future work could investigate federated privacy-preserving transparency enabling cross-platform insights while maintaining individual platform privacy protection.

Integration of user preferences in privacy budget allocation represents an important research direction enabling personalized privacy-utility trade-offs based on individual stakeholder requirements. Machine learning approaches for privacy parameter optimization could automate configuration processes while maintaining optimal performance across diverse operational scenarios [14].

Advanced research directions include exploration of privacy-preserving mechanisms for real-time revenue reporting systems and development of cross-platform transparency standards that maintain privacy protection across different creator economy ecosystems [15].

Acknowledgments

I would like to extend my sincere gratitude to H. K. Bhargava for his comprehensive research on the creator economy management as published in his article titled "The creator economy: Managing ecosystem supply, revenue sharing, and platform design" in Management Science (2022). His insights into platform design principles and revenue sharing mechanisms have significantly influenced my understanding of creator platform economics and have provided valuable inspiration for developing privacy-preserving transparency solutions in this critical domain.

I would like to express my heartfelt appreciation to Y. Zhao and J. Chen for their innovative survey on differential privacy for unstructured data content, as published in their article titled "A survey on differential privacy for unstructured data content" in ACM Computing Surveys (2022). Their comprehensive analysis of differential privacy mechanisms and applications have significantly enhanced my knowledge of privacy-preserving techniques and inspired my research in applying these methodologies to creator platform transparency systems.

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Cite this article

Zhang,J. (2025). Research on Revenue Transparency Mechanisms for Creator Platforms Based on Differential Privacy. Applied and Computational Engineering,184,38-49.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of CONF-MLA 2025 Symposium: Intelligent Systems and Automation: AI Models, IoT, and Robotic Algorithms

ISBN：978-1-80590-307-9(Print) / 978-1-80590-308-6(Online)

Editor：Hisham AbouGrad

Conference website: https://www.confmla.org/

Conference date: 17 November 2025

Series: Applied and Computational Engineering

Volume number: Vol.184

ISSN：2755-2721(Print) / 2755-273X(Online)

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