Security and privacy analysis of radio frequency identification systems
Bibliographic record
Abstract
Radio Frequency Identification (RFID) technology is widely used for variousapplications from access control to object tracking systems. Automation and fasterservices provided by this technology have striking effects on our daily life. However,there are several security and privacy concerns about RFID systems that remainunsolved. During the past years, several attacks have been designed against MifareClassic and HID iClass, two of the most widely used RFID systems on the market.The aim of this study was to improve the security and privacy mechanisms of RFIDsystems through the development of tools and the methodology of system analysis, inthe hope to find the possible flaws before the adversaries do. As an example, effortswere made to partially analyze OPUS cards (the RFID-enabled public transportationpasses in Montreal) and several security and privacy violating specifications of thesecards were highlighted. It was revealed that the static identification number of thecard is transfered in the anticollision process which can be used to track the cardholder without his consent. In addition, the information about the last three usages ofthe card (the time, the date and the metro/bus station) are transferred unencryptedand before the authentication process. Only a linear conversion is applied to theinformation which can be reversed by a simple application such as the one developedand provided in this study.Furthermore, design modifications to improve the security and privacy level of RFIDsystems were provided. These modifications are categorized based on the cost andthe disruption of service that the application of these modifications imposes to themanufacturing company.Key Words: RFID Systems, Privacy, Security, OPUS Cards
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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".