Strengthening AI Governance through Advanced Cryptographic Techniques

Authors

  • Alok Kumar Professor, Department of CSE Sir Padampat Singhania University, Udaipur, India
  • Utsav Upadhyay Assistant Professor, Department of CSE, Sir Padampat Singhania University, Udaipur, India.
  • Gajanand Sharma Associate Professor Department of CSE JECRC University, Jaipur, India.
  • Ravi Shankar Sharma Assistant Professor, Department of CSE, JECRC University, Jaipur, 303905, India.
  • Neha Mishra Assistant Professor, Department of CSE, JECRC University, Jaipur, India
  • Jitendra Kumawat Assistant Professor, Department of CSE, JECRC University, Jaipur, India

Keywords:

AI technologies, manuscript, accountability, governance, mitigating

Abstract

This research elucidates the pivotal role of advanced cryptographic techniques in fortifying the governance of artificial intelligence (AI) systems. Addressing the escalating challenges of accountability, transparency, and ethical AI development, the study explores the application of cryptography to enhance AI technologies' security, privacy, and accountability. The manuscript offers practical insights into cryptographic solutions, demonstrating their efficacy in mitigating risks and fostering responsible AI by combining a thorough literature review with empirical evidence. The findings contribute valuable perspectives for policymakers, practitioners, and researchers seeking to establish robust governance frameworks for the ethical deployment of AI technologies.

Downloads

Download data is not yet available.

References

Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., ... & Williams, M. D. (2021). Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57, 101994.

Mahajan, A., Vaidya, T., Gupta, A., Rane, S., & Gupta, S. (2019). Artificial intelligence in healthcare in developing nations: The beginning of a transformative journey. Cancer Research, Statistics, and Treatment, 2(2), 182-189.

Yigitcanlar, T., Corchado, J. M., Mehmood, R., Li, R. Y. M., Mossberger, K., & Desouza, K. (2021). Responsible urban innovation with local government artificial intelligence (AI): A conceptual framework and research agenda. Journal of Open Innovation: Technology, Market, and Complexity, 7(1), 71.

Taeihagh, A. (2021). Governance of artificial intelligence. Policy and society, 40(2), 137-157.

Díaz-Rodríguez, N., Del Ser, J., Coeckelbergh, M., de Prado, M. L., Herrera-Viedma, E., & Herrera, F. (2023). Connecting the dots in trustworthy Artificial Intelligence: From AI principles, ethics, and key requirements to responsible AI systems and regulation. Information Fusion, 101896.

Pike, E. R. (2019). Defending data: Toward ethical protections and comprehensive data governance. Emory LJ, 69, 687.

Habbal, A., Ali, M. K., & Abuzaraida, M. A. (2024). Artificial Intelligence Trust, Risk and Security Management (AI TRiSM): Frameworks, applications, challenges and future research directions. Expert Systems with Applications, 240, 122442.

Ireni-Saban, L., & Sherman, M. (2021). Ethical Governance of Artificial Intelligence in the Public Sector. Routledge.

de Almeida, P. G. R., dos Santos, C. D., & Farias, J. S. (2021). Artificial intelligence regulation: a framework for governance. Ethics and Information Technology, 23(3), 505-525.

Chhillar, D., & Aguilera, R. V. (2022). An eye for artificial intelligence: Insights into the governance of artificial intelligence and vision for future research. Business & Society, 61(5), 1197-1241.

Dai, D., & Boroomand, S. (2022). A review of artificial intelligence to enhance the security of big data systems: state-of-art, methodologies, applications, and challenges. Archives of Computational Methods in Engineering, 29(2), 1291-1309.

Nassar, M., Salah, K., ur Rehman, M. H., & Svetinovic, D. (2020). Blockchain for explainable and trustworthy artificial intelligence. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 10(1), e1340.

Zhao, C., Zhao, S., Zhao, M., Chen, Z., Gao, C. Z., Li, H., & Tan, Y. A. (2019). Secure multiparty computation: theory, practice and applications. Information Sciences, 476, 357-372.

Cheng, L., Varshney, K. R., & Liu, H. (2021). Socially responsible ai algorithms: Issues, purposes, and challenges. Journal of Artificial Intelligence Research, 71, 1137-1181.

Li, B., Qi, P., Liu, B., Di, S., Liu, J., Pei, J., ... & Zhou, B. (2023). Trustworthy AI: From principles to practices. ACM Computing Surveys, 55(9), 1-46.

Jafarigol, E. (2023). Uncovering the Potential of Federated Learning: Addressing Algorithmic and Data-driven Challenges under Privacy Restrictions.

Ahmad, K., Maabreh, M., Ghaly, M., Khan, K., Qadir, J., & Al-Fuqaha, A. (2022). Developing future human-centered smart cities: Critical analysis of smart city security, Data management, and Ethical challenges. Computer Science Review, 43, 100452.

Acar, A., Aksu, H., Uluagac, A. S., & Conti, M. (2018). A survey on homomorphic encryption schemes: Theory and implementation. ACM Computing Surveys (Csur), 51(4), 1-35.

Zhao, C., Zhao, S., Zhao, M., Chen, Z., Gao, C. Z., Li, H., & Tan, Y. A. (2019). Secure multiparty computation: theory, practice and applications. Information Sciences, 476, 357-372.

Du, W., & Atallah, M. J. (2001, September). Secure multiparty computation problems and their applications: a review and open problems. In Proceedings of the 2001 workshop on New security paradigms (pp. 13-22).

Ryffel, T. (2022). Cryptography for Privacy-Preserving Machine Learning (Doctoral dissertation, ENS Paris-Ecole Normale Supérieure de Paris).

Villegas-Ch, W., & García-Ortiz, J. (2023). Toward a Comprehensive Framework for Ensuring Security and Privacy in Artificial Intelligence. Electronics, 12(18), 3786.

Shneiderman, B. (2020). Bridging the gap between ethics and practice: guidelines for reliable, safe, and trustworthy human-centered AI systems. ACM Transactions on Interactive Intelligent Systems (TiiS), 10(4), 1-31.

Geng, J. (2023). Taking Computation to Data: Integrating Privacy-preserving AI techniques and Blockchain Allowing Secure Analysis of Sensitive Data on Premise.

Soria-Comas, J., Domingo-Ferrer, J., Sánchez, D., & Megías, D. (2017). Individual differential privacy: A utility-preserving formulation of differential privacy guarantees. IEEE Transactions on Information Forensics and Security, 12(6), 1418-1429.

Xu, R., Baracaldo, N., Zhou, Y., Anwar, A., & Ludwig, H. (2019, November). Hybridalpha: An efficient approach for privacy-preserving federated learning. In Proceedings of the 12th ACM workshop on artificial intelligence and security (pp. 13-23).

Kaissis, G. A., Makowski, M. R., Rückert, D., & Braren, R. F. (2020). Secure, privacy-preserving and federated machine learning in medical imaging. Nature Machine Intelligence, 2(6), 305-311.

Beltrán, E. T. M., Pérez, M. Q., Sánchez, P. M. S., Bernal, S. L., Bovet, G., Pérez, M. G., ... & Celdrán, A. H. (2023). Decentralized federated learning: Fundamentals, state of the art, frameworks, trends, and challenges. IEEE Communications Surveys & Tutorials.

Diro, A., Zhou, L., Saini, A., Kaisar, S., & Hiep, P. C. (2024). Leveraging zero knowledge proofs for blockchain-based identity sharing: A survey of advancements, challenges and opportunities. Journal of Information Security and Applications, 80, 103678.

Soria-Comas, J., Domingo-Ferrer, J., Sánchez, D., & Megías, D. (2017). Individual differential privacy: A utility-preserving formulation of differential privacy guarantees. IEEE Transactions on Information Forensics and Security, 12(6), 1418-1429.

Tyagi, A. K. (2024). Blockchain and Artificial Intelligence for Cyber Security in the Era of Internet of Things and Industrial Internet of Things Applications. In AI and Blockchain Applications in Industrial Robotics (pp. 171-199). IGI Global.

Zhang, P., Ding, S., & Zhao, Q. (2023). Exploiting Blockchain to Make AI Trustworthy: A Software Development Lifecycle View. ACM Computing Surveys.

Downloads

Published

23.02.2024

How to Cite

Kumar, A. ., Upadhyay, U. ., Sharma, G. ., Sharma, R. S. ., Mishra, N. ., & Kumawat, J. . (2024). Strengthening AI Governance through Advanced Cryptographic Techniques. International Journal of Intelligent Systems and Applications in Engineering, 12(17s), 553–560. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4920

Issue

Vol. 12 No. 17s (2024)

Section

Research Article

License

Copyright (c) 2024 Alok Kumar, Utsav Upadhyay, Gajanand Sharma, Ravi Shankar Sharma, Neha Mishra, Jitendra Kumawat

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.

IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.