Improving security and usability of low cost RFID tags
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Low cost RFID tags pose unique security challenges. Data tampering is one of such challenges that need to be addressed. In this paper, we propose a tamper detection solution for the EPC-Class1 Generation2 tag (a low cost passive RFID tag) based on a cryptographic PRNG (a pseudo random number generator for low cost RFID tags) function called LAMED and the Skew Tent chaotic map. Most of the existing solutions can only detect tampering in some portions (e.g. the OC or the EM field of EPC tag) of an RFID tag; in contrast, our solution can detect and discriminate tampering anywhere in the RFID tag. Moreover, unlike the existing tamper detection solutions, our proposal also includes a solution for cloning detection. Furthermore, this solution offers better security than the existing tamper detection solutions. Managing passwords for each individual tag is one of the main challenges for adopting security solutions in RFID applications, such as supply chain management; we address this issue in our solution by dramatically minimizing the load of password management and thus our solution becomes quite feasible for existing low cost RFID applications. Last, but not least, our proposed solution is compatible with the EPC Tag Data Standards for EPC Class1 Gen2 tags proposed by the EPCglobal and GS1 (Global Standardization Body).
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.
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 | 0.000 |
| 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 it