Generative AI and Privacy-Preserving Big Data Analytic in Cloud Environments with AI Agents

While generative artificial intelligence (GenAI) technologies are revolutionising content production, they also pose serious privacy and data security issues. The potential of privacy violations, biases, and cyberattacks rises as these models process large datasets, many of which contain sensitive or private data. These issues are examined in this book, especially in important fields like cybersecurity, healthcare, and finance. The potential for GenAI models to reproduce or infer sensitive data from training datasets is a major problem that raises ethical and intellectual property issues. Data protection techniques like encryption, tokenisation, and anonymisation are crucial to reducing these dangers. This study assesses the efficacy of these techniques by looking at how they affect the functional performance and privacy risk reduction of GenAI systems. It evaluates the impact of tokenisation and anonymisation on a state-of-the-art large language model (LLM) through experimental analysis. Empirical results offer insights into the trade-offs between protecting model performance and data privacy using open-source tools such as Microsoft Presidio. The goal of the research is to help create safe and morally sound GenAI applications, making sure that advancements in AI are in line with data security guidelines while preserving accuracy and efficiency in practical applications.

Privacy-Preserving, Big Data, AI-Driven,Cloud and Techniques

1. Chen Y, et al. Blockchain-based medical records secure storage and medical service framework. J Med Syst. 2019;43:1–9. [Google Scholar] [Crossref]

2. Mayer AH, da Costa CA, Righi RDR. Electronic health records in a blockchain: a systematic review. Health Inf J. 2020;26(2):1273–88. [Google Scholar] [Crossref]

3. Ghadi YY, et al. The role of blockchain to secure internet of medical things. Sci Rep. 2024;14(1):18422. [Google Scholar] [Crossref]

4. Ghadi YY, Shah SFA, Mazhar T, Shahzad T, Ouahada K, Hamam H. Enhancing patient healthcare with mobile edge computing and 5G: challenges and solutions for secure online health tools. J Cloud Comput. 2024;13(1):93. [Google Scholar] [Crossref]

5. Saranya R, Murugan A. A systematic review of enabling blockchain in healthcare system: Analysis, current status, challenges and future direction. Mater Today Proc. 2023;80:3010–5. [Google Scholar] [Crossref]

6. Andrew J, et al. Blockchain for healthcare systems: architecture, security challenges, trends and future directions. J Netw Comput Appl. 2023;215: 103633. [Google Scholar] [Crossref]

7. Sujan MA, Looking at the safety of ai from a systems perspective: two healthcare examples, in safety in the digital age: sociotechnical perspectives on algorithms and machine learning. 2023, Springer Nature Switzerland Cham. p. 79–90. . [Google Scholar] [Crossref]

8. Wehkamp K, Krawczak M, Schreiber S. The quality and utility of artifcial intelligence in patient care. Dtsch Arztebl Int. 2023;120(27–28):463. [Google Scholar] [Crossref]

9. Mondal H, Mondal S, Singla RK, Artifcial Intelligence in Rural Health in Developing Countries, in Artifcial Intelligence in Medical Virology. 2023, Springer. p. 37-48 [Google Scholar] [Crossref]

10. Zuhair V, et al. Exploring the impact of artifcial intelligence on global health and enhancing healthcare in developing nations. J Primary Care Commun Health. 2024;15:21501319241245850. [Google Scholar] [Crossref]

11. Poalelungi DG, et al. Advancing patient care: how artifcial intelligence is transforming healthcare. J Personal Med. 2023;13(8):1214. [Google Scholar] [Crossref]

12. Taherdoost H, Machine learning algorithms: features and applications, in Encyclopedia of Data Science and Machine Learning. 2023, IGI Global. 938–960. [Google Scholar] [Crossref]

13. Mirjalili S, Gandomi AH. Comprehensive metaheuristics: algorithms and applications. Amsterdam: Elsevier; 2023. [Google Scholar] [Crossref]

14. Worden K, et al. Artifcial neural networks, in machine learning in modeling and simulation: methods and applications. Berlin: Springer; 2023. p. 85–119. [Google Scholar] [Crossref]

15. Kasneci E, et al. ChatGPT for good? On opportunities and challenges of large language models for education. Learning Individual Dif. 2023;103: 102274. [Google Scholar] [Crossref]

16. Chang Y, et al. A survey on evaluation of large language models. ACM Trans Intell Syst Technol. 2024;15(3):1–45. [Google Scholar] [Crossref]

17. Chaka C. Detecting AI content in responses generated by ChatGPT, YouChat, and Chatsonic: The case of fve AI content detection tools. J Appl Learning Teaching. 2023;6(2):12. [Google Scholar] [Crossref]

18. Wu X, Duan R, Ni J. Unveiling security, privacy, and ethical concerns of ChatGPT. J Inf Intell. 2024;2(2):102–15. [Google Scholar] [Crossref]

19. Ma S, et al. “Are you sure?” Understanding the efects of human self-confdence calibration in ai-assisted decision making. in proceedings of the CHI conference on human factors in computing systems. 2024. [Google Scholar] [Crossref]

20. Masood I, et al. A blockchain-based system for patient data privacy and security. Multimedia Tools Applications. 2024;83(21):60443–67. [Google Scholar] [Crossref]

21. Salah M, Al Halbusi H, Abdelfattah F, May the force of text data analysis be with you: Unleashing the power of generative AI for social psychology research. Comput Hum Behav Artif Hum, 2023: 100006. [Google Scholar] [Crossref]

22. Al-Hawawreh M, Aljuhani A, Jararweh Y. Chatgpt for cybersecurity: practical applications, challenges, and future directions. Clust Comput. 2023;26(6):3421–36. [Google Scholar] [Crossref]

23. Yao Y, et al., A survey on large language model (llm) security and privacy: The good, the bad, and the ugly. High-Confdence Computing, 2024: p. 100211. [Google Scholar] [Crossref]

24. Singh K, Chatterjee S, Mariani M, Applications of generative AI and future organizational performance: The mediating role of explorative and exploitative innovation and the moderating role of ethical dilemmas and environmental dynamism. 2024. 133: 103021. [Google Scholar] [Crossref]

25. Tokayev K-J. Ethical implications of large language models a multidimensional exploration of societal, economic, and technical concerns. Int J Soc Anal. 2023;8(9):17–33. [Google Scholar] [Crossref]

26. Usman M, Qamar U. Secure electronic medical records storage and sharing using blockchain technology. Proc Comput Sci. 2020;174:321–7. [Google Scholar] [Crossref]

27. Vanathi J, G. SriPradha. BreakTheChain: A Proposed AI powered Mobile Application Framework to handle COVID-19 Pandemic. Alochana Chakra Journal. 9: 108–114. [Google Scholar] [Crossref]

28. Yan L, et al. Practical and ethical challenges of large language models in education: A systematic scoping review. Br J Edu Technol. 2024;55(1):90–112. [Google Scholar] [Crossref]

• "Towards GDP to GEP-Centric Model: A Proposed GEP Index Framework and Its Application in Haryana"
• Physicochemical Analysis of Petroleum Products in Selected Depots in Calabar Metropolis Cross River State and The Effects on Motor Engine
• Impact of Employee Welfare Measures on Workforce Well-Being in SBI: Insights from the Public Banking Sector
• Problem of Small-Scale Farmers in Agricutlure Sector in Tirunelveli Taluk
• Globalization, Economic Development, and Ecological Footprint in Tunisia: A QARDL Approach