State data security backed by Artificial Intelligence and Zero Knowledge Proofs in the context of sanctions and economic pressure
Bibliographic record
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.032 |
| Scholarly communication | 0.015 | 0.024 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".