How RDH Uses Entanglement Emulation to Protect Smart Cards, POS Terminals and Digital Banking from AI and Quantum Threats
Bibliographic record
Abstract
The global financial sector faces a perfect storm of emerging threats. Artificial intelligence is accelerating malware development and code decompilation, allowing attackers to reverse-engineer wallet apps and clone digital payment systems. At the same time, quantum computing is rapidly approaching the capability to break conventional encryption algorithms. Together, these forces threaten the very foundation of trust in online banking and e-commerce.The Randomized Data Handshake (RDH) introduces a new defense model for FinTech: a quantum-resilient, zero-trust encryption protocol designed specifically for mobile banking, e-commerce, and POS transactions. RDH wraps around lightweight ciphers such as ASCON and AES, enabling two parties to authenticate and create identical session keys without ever transmitting those keys or any sensitive account data. In practical terms, RDH can process a credit card purchase without ever transmitting the credit card numbers or data—eliminating the most common point of theft in financial systems.Unlike traditional software-based methods, RDH executes entirely within tamper-resistant hardware—smart cards, NFC tokens, or secure biometric dongles—ensuring that every cryptographic operation occurs only under explicit user control. A physical tap, button press, or fingerprint scan is required to activate a transaction. This architecture renders malware, spoofed apps, and relay-based fraud attempts useless because the handshake cannot be initiated or cloned without verified user presence.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".