Trust Without Transmission: How RDH Uses Entanglement Emulation to Protect Digital Banking from AI and Quantum Threats
Bibliographic record
Abstract
As artificial intelligence accelerates, its capacity to reverse-engineer, decompile, and imitate software systems now poses a direct threat to the foundation of digital security. AI-assisted tools can already analyze compiled binaries, infer encryption logic, and reconstruct wallet or payment workflows that were once considered opaque. At the same time, quantum computing looms as the next existential risk to classical encryption. Together, these twin pressures demand a new paradigm—one that moves security out of software and into tamper-resistant hardware.The Randomized Data Handshake (RDH) is a zero-trust encryption protocol engineered to counter both AI-based and quantum-based attacks. Acting as a lightweight wrapper around symmetric ciphers such as ASCON or AES, RDH allows two devices to derive identical session keys without ever transmitting them. Instead of sending secrets, RDH exchanges randomized challenge instructions that each side uses to construct keys locally, producing communication that contains no interpretable data in transit. This design not only removes the attack surface exploited by AI decompilation, but also emulates the information-exchange properties of quantum entanglement—where no usable key material ever leaves the device.RDH is optimized for FinTech, IoT, and e-commerce environments where hardware constraints, latency, and trust boundaries intersect. It can be embedded in smart cards, NFC tokens, USB dongles, or dedicated processors, ensuring that all cryptographic operations occur within secure hardware under explicit user control. Biometric sensors or motion triggers confirm physical presence, preventing malware or spoofed applications from activating encryption without consent. Because it builds on existing symmetric standards, RDH offers quantum-resilient security with minimal computational overhead, avoiding the performance penalties of current lattice-based algorithms.In an era where AI can read, clone, and manipulate code faster than humans can patch it, RDH redefines the trust model: software may be compromised, but hardware cannot be impersonated. The protocol transforms encryption from a passive defense into an active, user-anchored verification process—one that resists AI inference, survives quantum attack, and restores confidence in digital authentication.By fusing AI awareness, zero-trust architecture, and post-quantum cryptography, RDH establishes a deployable framework for the next generation of secure communication—bridging the gap between hardware control and the AI-driven threat landscape.www.srcmeetings.com26International Conference on Artificial Intelligence and Cybersecurity (ICAIC 2025)November 27-28, 2025 (Virtual)Conference Proceedings
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.005 | 0.011 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".