Research on Revenue Transparency Mechanisms for Creator Platforms Based on Differential Privacy
Research Article
Open Access
CC BY

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

Jin Zhang 1*
1 Computer Science, Illinois Institute of Technology, IL, USA
*Corresponding author: iu45785@gmail.com
Published on 24 September 2025
Journal Cover
ACE Vol.184
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-80590-307-9
ISBN (Online): 978-1-80590-308-6
Download Cover

Abstract

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 ε-differential privacy with configurable privacy parameters ranging from 0.1 to 2.0, enabling platforms to balance transparency requirements with privacy constraints.

Keywords:

differential privacy, creator economy, revenue transparency, platform governance

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

References

[1]. Bhargava, H. K. (2022). The creator economy: Managing ecosystem supply, revenue sharing, and platform design. Management Science, 68(7), 5233-5251.

[2]. Azad, M. A., Perera, C., Bag, S., Barhamgi, M., & Hao, F. (2020). Privacy-preserving crowd-sensed trust aggregation in the user-centeric Internet of people networks. ACM Transactions on Cyber-Physical Systems, 5(1), 1-24.

[3]. Wang, Y., Wang, Q., Zhao, L., & Wang, C. (2023). Differential privacy in deep learning: Privacy and beyond. Future Generation Computer Systems, 148, 408-424.

[4]. Singla, B., Shalender, K., & Singh, N. (Eds.). (2024). Creator's Economy in Metaverse Platforms: Empowering Stakeholders Through Omnichannel Approach: Empowering Stakeholders Through Omnichannel Approach. IGI Global.

[5]. Vasa, J., & Thakkar, A. (2023). Deep learning: Differential privacy preservation in the era of big data. Journal of computer information systems, 63(3), 608-631.

[6]. Sai, S., Hassija, V., Chamola, V., & Guizani, M. (2023). Federated learning and NFT-based privacy-preserving medical-data-sharing scheme for intelligent diagnosis in smart healthcare. IEEE Internet of Things Journal, 11(4), 5568-5577.

[7]. Zhao, Y., & Chen, J. (2022). A survey on differential privacy for unstructured data content. ACM Computing Surveys (CSUR), 54(10s), 1-28.

[8]. Leng, Y., Chen, Y., Dong, X., Wu, J., & Shi, G. (2021). Social interaction leakages from public behavioral data: A diagnostic and differential privacy framework. Available at SSRN 3875878.

[9]. Wang, H., Ning, H., Lin, Y., Wang, W., Dhelim, S., Farha, F., ... & Daneshmand, M. (2023). A survey on the metaverse: The state-of-the-art, technologies, applications, and challenges. IEEE Internet of Things Journal, 10(16), 14671-14688.

[10]. Werder, K., Ramesh, B., & Zhang, R. (2022). Establishing data provenance for responsible artificial intelligence systems. ACM Transactions on Management Information Systems (TMIS), 13(2), 1-23.

[11]. Huang, C., Zhang, Z., Mao, B., & Yao, X. (2022). An overview of artificial intelligence ethics. IEEE Transactions on Artificial Intelligence, 4(4), 799-819.

[12]. Kenthapadi, K., & Tran, T. T. (2018, October). Pripearl: A framework for privacy-preserving analytics and reporting at linkedin. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management (pp. 2183-2191).

[13]. Gupta, B., & Mangal, A. (2024). Metaverse & Privacy: Navigating Legal and Security Concerns Under Data Protection Regulations. Available at SSRN 4728595.

[14]. Yazdinejad, A., & Kong, J. D. (2025). Breaking Interprovincial Data Silos: How Federated Learning Can Unlock Canada's Public Health Potential. Available at SSRN 5247328.

[15]. Lee, Y. (2025). Digital fashion ideology: Towards a critical public sphere. International Journal of Cultural Studies, 13678779251351644.

References

[1]. Bhargava, H. K. (2022). The creator economy: Managing ecosystem supply, revenue sharing, and platform design. Management Science, 68(7), 5233-5251.

[2]. Azad, M. A., Perera, C., Bag, S., Barhamgi, M., & Hao, F. (2020). Privacy-preserving crowd-sensed trust aggregation in the user-centeric Internet of people networks. ACM Transactions on Cyber-Physical Systems, 5(1), 1-24.

[3]. Wang, Y., Wang, Q., Zhao, L., & Wang, C. (2023). Differential privacy in deep learning: Privacy and beyond. Future Generation Computer Systems, 148, 408-424.

[4]. Singla, B., Shalender, K., & Singh, N. (Eds.). (2024). Creator's Economy in Metaverse Platforms: Empowering Stakeholders Through Omnichannel Approach: Empowering Stakeholders Through Omnichannel Approach. IGI Global.

[5]. Vasa, J., & Thakkar, A. (2023). Deep learning: Differential privacy preservation in the era of big data. Journal of computer information systems, 63(3), 608-631.

[6]. Sai, S., Hassija, V., Chamola, V., & Guizani, M. (2023). Federated learning and NFT-based privacy-preserving medical-data-sharing scheme for intelligent diagnosis in smart healthcare. IEEE Internet of Things Journal, 11(4), 5568-5577.

[7]. Zhao, Y., & Chen, J. (2022). A survey on differential privacy for unstructured data content. ACM Computing Surveys (CSUR), 54(10s), 1-28.

[8]. Leng, Y., Chen, Y., Dong, X., Wu, J., & Shi, G. (2021). Social interaction leakages from public behavioral data: A diagnostic and differential privacy framework. Available at SSRN 3875878.

[9]. Wang, H., Ning, H., Lin, Y., Wang, W., Dhelim, S., Farha, F., ... & Daneshmand, M. (2023). A survey on the metaverse: The state-of-the-art, technologies, applications, and challenges. IEEE Internet of Things Journal, 10(16), 14671-14688.

[10]. Werder, K., Ramesh, B., & Zhang, R. (2022). Establishing data provenance for responsible artificial intelligence systems. ACM Transactions on Management Information Systems (TMIS), 13(2), 1-23.

[11]. Huang, C., Zhang, Z., Mao, B., & Yao, X. (2022). An overview of artificial intelligence ethics. IEEE Transactions on Artificial Intelligence, 4(4), 799-819.

[12]. Kenthapadi, K., & Tran, T. T. (2018, October). Pripearl: A framework for privacy-preserving analytics and reporting at linkedin. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management (pp. 2183-2191).

[13]. Gupta, B., & Mangal, A. (2024). Metaverse & Privacy: Navigating Legal and Security Concerns Under Data Protection Regulations. Available at SSRN 4728595.

[14]. Yazdinejad, A., & Kong, J. D. (2025). Breaking Interprovincial Data Silos: How Federated Learning Can Unlock Canada's Public Health Potential. Available at SSRN 5247328.

[15]. Lee, Y. (2025). Digital fashion ideology: Towards a critical public sphere. International Journal of Cultural Studies, 13678779251351644.

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.

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)

© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license. Authors who publish this series agree to the following terms:

1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this series.

2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this series.

3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See Open access policy for details).

For authors
For editors
For reviewers

Cookies are used by this site.
Copyright © 2024 EWA Publishing, its licensors, and contributors. All rights reserved, including text and data mining, AI training, and similar technologies.