State data security backed by Artificial Intelligence and Zero Knowledge Proofs in the context of sanctions and economic pressure
Economic Annals-XXI: Volume 202, Issue (3-4), Pages: 4-16
Citation information:
Jalili, A. Q., & Dziatkovskii, A. (2023). State data security backed by Artificial Intelligence and Zero Knowledge Proofs in the context of sanctions and economic pressure. Economic Annals-XXI, 202(3-4), 4-16. doi: https://doi.org/10.21003/ea.V202-01
Abdul Qawi Jalili
MA (Economics),
Chief Business Development Officer,
GOTBIT CONSULTING LLC, USA
175 Piccadilly, St. James’s, London, W1J 9TB, United Kingdom
Qawi@gotbit.io
ORCID ID: https://orcid.org/0000-0003-0701-5777
Anton Dziatkovskii
PhD (Pedagogy),
Expert in Artificial Intelligence,
Zero Knowledge Proof, Blockchain, and Data Science;
CEO,
Platinum VC & Incubator Australia
Level 8, 7 Macquarie Place, Sydney, NSW 2000, Australia
a@platinum0x.com.au
ORCID ID: https://orcid.org/0000-0001-7408-3054
State data security backed by Artificial Intelligence and Zero Knowledge Proofs in the context of sanctions and economic pressure
Abstract. This research paper aims to elucidate the intricate relationship between artificial intelligence (AI), state data security, and the volatile circumstances induced by sanctions and economic pressure. By undertaking a comprehensive literature review, the study not only offers a historical context of state data security mechanisms but also delves deeply into the advancements provided by AI-driven solutions. The work serves as a crucial reference for policymakers, cybersecurity experts, and academic researchers, laying a foundation for the nuanced understanding of AI’s capabilities and limitations within the realms of state data security and economic stressors.
Employing an analytical framework, the paper systematically distills knowledge from a wide array of sources, including academic articles, technical reports, policy briefs, and international standards. This multidimensional analysis allows for a holistic understanding of the state-of-the-art AI technologies, their applicability in fortifying state data security, and the ethical labyrinth that states must navigate.
Paper underscores a multitude of challenges and ethical considerations that are often overshadowed by the technological prowess of AI. These encompass issues such as data privacy infringement, potential for mass surveillance, and ethical quandaries around bias and discrimination. The paper also throws light on the pivotal factors of accountability and transparency, essential for maintaining public trust in AI-augmented state security mechanisms. The study raises awareness about AI-driven cyber threats, focusing on the paradox of employing AI to enhance security while also becoming susceptible to advanced AI-driven cyberattacks. Paper addresses the long-term sustainability and resilience of AI-enabled security measures, particularly in the context of evolving cyber threats and the inherent instability brought about by economic pressures and sanctions. The resilience of AI algorithms and systems under these specific conditions is scrutinized, offering a forward-looking perspective on the adaptability and robustness of AI technologies in safeguarding state data.
Keywords: State Data; AI; Security; Cybersecurity; Sanctions; Zero-Knowledge Proof (ZKP); Algorithm; Ethics
JEL Classifications: H56; O33; O38; F51; K42
Acknowledgements and Funding: The authors received no direct funding for this research.
Contribution: The authors contributed equally to this work
Data Availability Statement: The dataset is available from the authors upon request.
DOI: https://doi.org/10.21003/ea.V202-01
References
- Abadi, M., & Andersen, D. G. (2016). Learning to protect communications with adversarial neural cryptography. arXiv.
https://doi.org/10.48550/arXiv.1610.06918 - Center for Strategic and International Studies (CSIS). (2019). Financial Sector Cybersecurity Requirements in the Asia-Pacific Region.
https://csis-website-prod.s3.amazonaws.com/s3fs-public/publication/190429_CarterCrumpler_APAC_WEB.pdf - European Commission. (2022). Investment in AI-driven security education and research: Opportunities and challenges. European Commission Report.
https://digital-strategy.ec.europa.eu/en/policies/european-approach-artificial-intelligence - Gartner. (2019). Gartner predicts 75% of enterprises will shift from piloting to operationalizing AI by 2024.
https://www.gartner.com/en/newsroom/press-releases/2021-03-16-
gartner-identifies-top-10-data-and-analytics-technologies-trends-for-2021 - Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press.
- Guo, H., Cheng, J., Wang, J., Chen, T., Yuan, Y., Li, H., & Sheng, V. S. (2022). IoT Data Blockchain-Based Transaction Model Using Zero-Knowledge Proofs and Proxy Re-encryption. In Sun, X., Zhang, X., Xia, Z., & Bertino, E. (Eds.) Artificial Intelligence and Security. ICAIS 2022. Lecture Notes in Computer Science, 13339, 882-895.
https://doi.org/10.1007/978-3-031-06788-4_48 - IBM. (2023). Cost of a data breach report.
https://www.ibm.com/security/data-breach - Jagielski, M., Oprea, A., Biggio, B., Liu, C., Nita-Rotaru, C., & Li, B. (2018). Manipulating machine learning: Poisoning attacks and countermeasures for regression learning. IEEE Symposium on Security and Privacy (SP) (pp. 19-35). San Francisco, CA, USA.
https://doi.org/10.1109/SP.2018.00057 - Kandias, M., Mitrou, L., Stavrou, V., & Gritzalis, D. (2013). Which side are you on? A new Panopticon vs. privacy. Information Systems Frontiers, 15(3), 427-445.
https://doi.org/10.5220/0004516500980110 - Knight, W. (2017). The dark secret at the heart of AI. MIT Technology Review, 120(3), 54-65.
https://www.technologyreview.com/2017/04/11/5113/the-dark-secret-at-the-heart-of-ai/ - Kshetri, N. (2018). Will blockchain emerge as a tool to break the poverty chain in the Global South? Third World Quarterly, 39(8), 1478-1496.
https://doi.org/10.1080/01436597.2017.1298438 - Morais, E., Koens, T., van Wijk, C., & Koren, A. (2020). A Survey on Zero Knowledge Range Proofs and Applications. ArXiv, Computer Science.
https://doi.org/10.48550/arXiv.1907.06381 - Moya, C. V., Bermejo Higuera, J. R., Higuera, J. B., & Sicilia Montalvo, J. A. (2023). Implementation and Security Test of Zero-Knowledge Protocols on SSI Blockchain. Applied Sciences, 13(9), 5552.
https://doi.org/10.3390/app13095552 - OECD. (2019). AI governance frameworks: Principles and recommendations. OECD Digital Economy Papers, No. 274.
https://doi.org/10.1787/20716826 - Satrajit, G., & Aniket, K. (2015). Post-quantum forward secure onion routing. Proceedings of the ACM on Measurement and Analysis of Computing Systems, 2(1), 1-22.
https://eprint.iacr.org/2015/008.pdf - Scherer, M. U. (2016). Regulating artificial intelligence systems: Risks, challenges, competencies, and strategies. Harvard Journal of Law & Technology, 29(2), 353-400.
https://doi.org/10.2139/ssrn.2609777 - Shokri, R., & Shmatikov, V. (2015). Privacy-preserving deep learning. Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security, 1310-1321.
https://doi.org/10.1145/2810103.2813687 - Singh, N., Dayama, P., & Pandit. V., (2021). Zero Knowledge Proofs Towards Verifiable Decentralized AI Pipelines. Cryptology ePrint Archive, Paper 2021/1633.
https://ia.cr/2021/1633 - Souza, R. R., Coelho, F. C., Shah, R., & Connelly, M. (2016). Using Artificial Intelligence to Identify State Secrets. ArXiv, Computer Science.
https://doi.org/10.48550/arXiv.1611.00356 - United Nations. (2023). International Community Must Urgently Confront New Reality of Generative, Artificial Intelligence, Speakers Stress as Security Council Debates Risks, Rewards. United Nations General Assembly SC/15359.
https://press.un.org/en/2023/sc15359.doc.htm#:~:text=The%20international%20
community%20must%20urgently,inherent%20in%20this%20emerging%20technology - Uzun, M. (2020). Artificial Intelligence and State Economic Security. In Bilgin, M., Danis, H., Demir, E., & Tony-Okeke, U. (Eds.) Eurasian Economic Perspectives. Eurasian Studies in Business and Economics, 15(1). Springer, Cham.
https://doi.org/10.1007/978-3-030-48531-3_13 - Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2023). Attention is all you need. ARXIV, (version, v7).
https://doi.org/10.48550/arXiv.1706.03762 - Xuefei, Y., Yanming, Z., & Jiankun, H. (2021). A Comprehensive Survey of Privacy-preserving Federated Learning: A Taxonomy, Review, and Future Directions. ACM Computing Surveys, 54(6), 131.
https://doi.org/10.1145/3460427 - Zarsky, T. (2016). The trouble with algorithmic decisions: An analytic road map to examine efficiency and fairness in automated and opaque decision making. Science, Technology, & Human Values, 41(1), 118-132.
https://doi.org/10.1177/0162243915605575
Received 20.01.2023
Received in revised form 12.02.2023
Accepted 16.02.2023
Available online 10.04.2023