School of Professional Studies
Document Type
Article
Abstract
This paper presents a systematic bibliometric analysis of the artificial intelligence (AI) domain to explore privacy protection research as AI technologies integrate and data privacy concerns rise. Understanding evolutionary patterns and current trends in this research is crucial. Leveraging bibliometric techniques, the authors analyze 8,322 papers from the Web of Science (WoS) database, spanning 1990 to 2023. The analysis highlights IEEE Transactions on Knowledge and Data Engineering and IEEE Access journals as highly influential, the former being an early contributor and the latter emerging as a pivotal source. The study demonstrates substantial disparities in scientific productivity across countries. Specifically, the top 10 countries collectively accounted for 74.8% of the articles, with China and the USA making up nearly half of the total contribution (46.1%). In contrast, regions in Africa and South America exhibited lower scientific production. The evolution of privacy preservation research is reflected, shifting from an algorithm-oriented approach to a focus on data orientation, and subsequently, to privacy solutions centered around Cloud Computing. In recent years, there has been a shift towards embracing Federated Learning and Differential Privacy. The analysis brings to light emerging themes and identifies research gaps, notably a global disparity in research output and a lag in ethical and legal inquiry. It asserts that enhanced interdisciplinary collaboration is imperative to formulate comprehensive privacy solutions for AI. Specifically, the paper imparts invaluable insights that are pivotal for effectively addressing the evolving privacy concerns in the era of AI and big data.
Publication Title
IEEE Access
Publication Date
3-18-2024
Volume
12
First Page
41704
Last Page
41726
ISSN
2169-3536
DOI
10.1109/ACCESS.2024.3378126
Keywords
Artificial intelligence, Privacy, Data privacy, Bibliometrics, Systematics, Search problems, Market research, Homomorphic encryption, Privacy
Repository Citation
Yu, Shasha, "Insights Into Privacy Protection Research in AI" (2024). School of Professional Studies. 2.
https://commons.clarku.edu/sops_fac/2
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright
Yu, S., Carroll, F., & Bentley, B. L. (2024). Insights Into Privacy Protection Research in AI. IEEE Access, 12, 41704-41726.