Data Privacy Studies (2010–2026): A Scopus-Based Bibliometric Analysis of Research Hotspots and Citation Dynamics
DOI:
https://doi.org/10.58812/wsist.v4i01.2831Keywords:
Data Privacy, Bibliometric Analysis, Scopus, VOSviewer, Research HotspotsAbstract
The current study is designed to analyze the research on data privacy between the years 2010 and 2026 using bibliometrics. It seeks to explore collaboration trends, influential literature, and emerging research trends within the research area through co-authorship, citations, and co-keywords analysis. VOSViewer was used for the analysis, as well as visualization of co-citation and co-keyword networks, thus mapping out the intellectual and conceptual network of data privacy. As can be seen from the results, there have been many developments regarding the field under review, marked by increased international collaboration, especially among top-ranking countries such as China, the United Kingdom, India, and Germany. The citation network shows that the field relies not only on technologies like differential privacy and machine learning but also on behavior-based factors such as trust and privacy. The keyword network further indicates that "data privacy" plays an important role in this research domain, together with artificial intelligence, deep learning, and federated learning. The change in time for keywords implies a transition from traditional security methods to privacy-oriented techniques.
References
[1] A. S. Ali, Z. F. Zaaba, and M. M. Singh, “The rise of ‘security and privacy’: bibliometric analysis of computer privacy research,” Int. J. Inf. Secur., vol. 23, no. 2, pp. 863–885, 2024.
[2] S. Hossain, M. Amees, H. Zaffar, and N. Ahmad, “Measuring scholarly influence: a bibliometric analysis of data privacy on social media research,” Inf. Comput. Secur., vol. 34, no. 1, pp. 47–65, 2026.
[3] K. Kumar and M. Khari, “A privacy preserving mechanisms to secure data driven approaches in industrial internet of things: A bibliometric analysis,” Peer-to-Peer Netw. Appl., vol. 19, no. 3, p. 70, 2026.
[4] İ. H. Efendioğlu, “Marketing and Data Privacy: A Bibliometric Analysis,” Kocaeli Üniversitesi Sos. Bilim. Derg., vol. 2, no. 48, pp. 14–42, 2024.
[5] A. Valencia-Arias, J. D. González-Ruiz, L. Verde Flores, L. Vega-Mori, P. Rodríguez-Correa, and G. Sánchez Santos, “Machine learning and blockchain: A bibliometric study on security and privacy,” Information, vol. 15, no. 1, p. 65, 2024.
[6] W. Shi, J. Cao, Q. Zhang, Y. Li, and L. Xu, “Edge computing: Vision and challenges,” IEEE internet things J., vol. 3, no. 5, pp. 637–646, 2016.
[7] C. Dwork, F. McSherry, K. Nissim, and A. Smith, “Calibrating noise to sensitivity in private data analysis,” in Theory of cryptography conference, Springer, 2006, pp. 265–284.
[8] D. Boyd and K. Crawford, “Critical questions for big data: Provocations for a cultural, technological, and scholarly phenomenon,” Information, Commun. Soc., vol. 15, no. 5, pp. 662–679, 2012.
[9] F. Meyer et al., “The metagenomics RAST server–a public resource for the automatic phylogenetic and functional analysis of metagenomes,” BMC Bioinformatics, vol. 9, no. 1, p. 386, 2008.
[10] D. J. Kim, D. L. Ferrin, and H. R. Rao, “A trust-based consumer decision-making model in electronic commerce: The role of trust, perceived risk, and their antecedents,” Decis. Support Syst., vol. 44, no. 2, pp. 544–564, 2008.
[11] N. K. Malhotra, S. S. Kim, and J. Agarwal, “Internet users’ information privacy concerns (IUIPC): The construct, the scale, and a causal model,” Inf. Syst. Res., vol. 15, no. 4, pp. 336–355, 2004.
[12] D. Berthelot, N. Carlini, I. Goodfellow, N. Papernot, A. Oliver, and C. A. Raffel, “Mixmatch: A holistic approach to semi-supervised learning,” Adv. Neural Inf. Process. Syst., vol. 32, 2019.
[13] M. Fredrikson, S. Jha, and T. Ristenpart, “Model inversion attacks that exploit confidence information and basic countermeasures,” in Proceedings of the 22nd ACM SIGSAC conference on computer and communications security, 2015, pp. 1322–1333.
[14] S. Wolfert, L. Ge, C. Verdouw, and M.-J. Bogaardt, “Big data in smart farming–a review,” Agric. Syst., vol. 153, pp. 69–80, 2017.
[15] P. A. Pavlou, H. Liang, and Y. Xue, “Understanding and mitigating uncertainty in online exchange relationships: a principal–agent perspective1,” MIS Q., vol. 31, no. 1, pp. 105–136, 2007.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Loso Judijanto
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Facebook 
Instagram 
Twitter 
Youtube 
westscience.journals@gmail.com