RFID Security : Tiny Encryption Algorithm And Authentication Protocols
Bibliographic record
Abstract
With recent advancement in Radio Frequency Identification (RFID) technology, in addition to reduction in cost of each unit, security has emerged as a major concern. Since an RFID tag has limited resources like memory, power and processing capabilities, authentication must be provided by encryption and decryption procedures that are lightweight consuming minimal resources. This report investigates some relevant RFID encryption algorithms and their possible implementations with respect to security, cost and performance. A survey and brief comparison of the algorithms are performed and the Tiny Encryption Algorithm (TEA) is selected as a feasible solution for encryption and decryption with an acceptable level of security. TEA is implemented on an FPGA (Field Programmable Gate Array) platform. After investigating several state-of-the-art authentication approaches, two protocols are designed incorporating TEA and implemented using VHDL. Simulations corroborate the functionality of the protocols and the two techniques are compared in terms of timing, cost, security and performance. Potential improvements to enhance the security and strengthen RF communication during authentication are explored.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".