A Consumer Data Privacy Protection Model Based on Non-Parametric Statistics for Dynamic Data Publishing in e-Commerce Platforms

Jiao Jia


Objectives: Consumer data privacy on e-commerce platforms is increasingly crucial. This study aims to investigate privacy protection mechanisms, particularly focusing on personal and corporate secrets. It seeks to understand individual perspectives on privacy and preferences for data disclosure. The primary objective is to explore methods for safeguarding personal information while maintaining data integrity. Methods/Analysis: We employ non-parametric statistical techniques to analyze consumer behavior and preferences on e-commerce platforms. This involves examining patterns of data disclosure and identifying sensitive information shared by users. By studying communication dynamics and recording practices, we assess the efficacy of current privacy protection measures. Novelty/Improvement: This study contributes to the understanding of consumer privacy protection by emphasizing the importance of non-parametric statistical methods in e-commerce research. Our findings underscore the need for enhanced privacy measures. We advocate for further research and development of innovative privacy-enhancing technologies to address evolving privacy challenges in online commerce. Findings:Our research highlights the significance of personal privacy concerns in e-commerce settings. We identify a spectrum of privacy attitudes among users, ranging from strict confidentiality to selective disclosure. Furthermore, our analysis reveals potential vulnerabilities in current privacy safeguards, particularly regarding the collection and storage of sensitive data on e-commerce platforms.


Doi: 10.28991/HIJ-2024-05-02-013

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Non-Parametric Statistics; Dynamic Data; Electronic Commerce; Consumer Privacy.


Yuniar, A. D. (2024). Thin privacy boundaries: proximity and accessibility of E-commerce privacy policy in young consumers of Indonesia. International Journal of Social Economics, 1-11. doi:10.1108/IJSE-11-2022-0740.

Tao, S., Liu, Y., & Sun, C. (2024). Understanding information sensitivity perceptions and its impact on information privacy concerns in e-commerce services: Insights from China. Computers and Security, 138, 103646. doi:10.1016/j.cose.2023.103646.

Tao, S., Liu, Y., & Sun, C. (2024). Examining the inconsistent effect of privacy control on privacy concerns in e-commerce services: The moderating role of privacy experience and risk propensity. Computers and Security, 140. doi:10.1016/j.cose.2024.103794.

Mutambik, I., Lee, J., Almuqrin, A., Zhang, J. Z., & Homadi, A. (2023). The Growth of Social Commerce: How It Is Affected by Users’ Privacy Concerns. Journal of Theoretical and Applied Electronic Commerce Research, 18(1), 725–743. doi:10.3390/jtaer18010037.

Paulson, G. (2024). Assessing data phishing risks associated with unencrypted apps on smartphones with non-parametric test and random forest model: Insights from Kuwait phishing scam calls. Journal of Engineering Research (Kuwait), 1-7. doi:10.1016/j.jer.2023.09.017.

Verdier, H., Laurent, F., Cassé, A., Vestergaard, C. L., Specht, C. G., & Masson, J. B. (2023). Simulation-based inference for nonparametric statistical comparison of biomolecule dynamics. PLoS Computational Biology, 19(2), 1010088. doi:10.1371/journal.pcbi.1010088.

Xu, Z., Yang, Y., Gao, X., & Hu, M. (2023). DCFF-MTAD: A Multivariate Time-Series Anomaly Detection Model Based on Dual-Channel Feature Fusion. Sensors, 23(8), 3910. doi:10.3390/s23083910.

Liu, R., & Wang, E. (2023). Blockchain and mobile client privacy protection in e-commerce consumer shopping tendency identification application. Soft Computing, 27(9), 6019–6031. doi:10.1007/s00500-023-08099-8.

Srivastava, S., & Shobhna Jeet, D. R. (2023). E-Commerce and Privacy Issues. Russian Law Journal, XI(5), 2170-2175.

Shen, H., Wu, G., Xia, Z., Susilo, W., & Zhang, M. (2023). A Privacy-Preserving and Verifiable Statistical Analysis Scheme for an E-Commerce Platform. IEEE Transactions on Information Forensics and Security, 18, 2637–2652. doi:10.1109/TIFS.2023.3269669.

Ren, X. Y., Zhang, P., & Zhou, Y. Q. (2019). Distinct model on privacy protection of dynamic data publication. Cluster Computing, 22, 15127-15136. doi:10.1007/s10586-018-2506-3.

Sharma, K. (2010). An Evaluation of Consumer Privacy Protection in E-Commerce Websites: A Comparative Study of Six E-Stores: Part II. EDPACS, 42(2), 1-19. doi:10.1080/07366981.2010.526040.

Bresson, G., & Logossah, K. (2011). Crowding-out effects of cruise tourism on stay-over tourism in the Caribbean: Non-parametric panel data evidence. Tourism Economics, 17(1), 127-158. doi:10.5367/te.2011.0028.

Wu, Y., Wang, W., Toll, M., Alkhoury, W., Sauter, M., & Kolditz, O. (2011). Development of a 3D groundwater model based on scarce data: The Wadi Kafrein catchment/Jordan. Environmental Earth Sciences, 64, 771-785. doi:10.1007/s12665-010-0898-3.

Yonghui, Z., Su, L., & Phillips, P. C. (2011). Testing for Common Trends in Semiparametric Panel Data Models with Fixed Effects. Cowles Foundation Discussion Paper No. 1832. doi:10.2139/ssrn.1951892.

Yang, C. (2011). Analysis on protection of e-commerce consumer network privacy. Procedia Engineering, 15, 5519-5524. doi:10.1016/j.proeng.2011.08.1024.

Diwandari, S., & Hidayat, A. T. (2021, March). Comparison of classification performance based on dynamic mining of user interest navigation pattern in e-commerce websites. In Journal of Physics: Conference Series: IOP Publishing, 1844(1), 012025. doi:10.1088/1742-6596/1844/1/012025.

Wang, X., Dai, H. N., & Zhang, K. (2019). Secure and flexible economic data sharing protocol based on ID-based dynamic exclusive broadcast encryption in economic system. Future Generation Computer Systems, 99, 177–185. doi:10.1016/j.future.2018.11.013.

Sreedevi, E. P., Kattumannil, S. K., & Dewan, I. (2021). A non-parametric test for independence of time to failure and cause of failure for discrete competing risks data. Statistics, 55(5), 1107-1122. doi:10.1080/02331888.2021.1975712.

Serrano, E., Such, J. M., Botía, J. A., & García-Fornes, A. (2014). Strategies for avoiding preference profiling in agent-based e-commerce environments. Applied Intelligence, 40(1), 127–142. doi:10.1007/s10489-013-0448-2.

Qiuyang, G., Qilian, N., Xiangzhao, M., & Zhijiao, Y. (2019). Dynamic social privacy protection based on graph mode partition in complex social network. Personal and Ubiquitous Computing, 23, 511-519. doi:10.1007/s00779-019-01249-6.

Ding, H., Peng, C., Tian, Y., & Xiang, S. (2019). A risk adaptive access control model based on Markov for big data in the cloud. International Journal of High-Performance Computing and Networking, 13(4), 464-475. doi:10.1504/IJHPCN.2019.099269.

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DOI: 10.28991/HIJ-2024-05-02-013


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