Privacy Issues of Applying RFID in Retail Industry
Bibliographic record
Abstract
Retail industry poses typical enterprise computing challenges, since a retailer normally deals with multiple parties that belong to different organizations (i.e., suppliers, manufacturers, distributors, end consumers). Capable of enabling retailers to effectively and efficiently manage merchandise transferring among various parties, Radio Frequency Identification (RFID) is an emerging technology that potentially could revolutionize the way retailers do business. With the dramatic price drop of RFID tags, it is possible that RFID could be applied to each item sold by a retailer. However, RFID technology poses critical privacy challenges. If not properly used, the data stored in RFID could be abused and, thus, cause privacy concerns for end consumers. In this article, we first analyze the potential privacy issue of RFID utilization. Then we propose a privacy authorization model that aims to precisely define comprehensive RFID privacy policies. Extended from the role-based access control model, our privacy authorization model ensures the special needs of RFID-related privacy protection. These policies are designed from the perspective of end consumers, whose privacy rights potentially could be violated. Finally, we explore the feasibility of applying Enterprise Privacy Authorization Language (EPAL) as the vehicle for specifying RFID-related privacy rules.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".