Decentralized Identifiers and Verifiable Credentials for Smartphone Anticounterfeiting and Decentralized IMEI Database
Bibliographic record
Abstract
The smartphone industry is lucrative for device counterfeiting with over 1.5 billion devices sold annually in the last three years. In 2017, it is estimated that there were around 184 million counterfeit devices, valued at 45.3 billion EUR, 12.9% of total sales. Beyond its economic impact, smartphone counterfeiting affects various aspects of user security and privacy, harms manufacturer reputation, and degrades service quality. Furthermore, since smartphone devices are attached to different mobile networks globally, challenges arise on how devices’ identities are maintained and verified and how the supply chain actors can access the device identity throughout its life cycle with less control from third parties. Decentralized identifiers (DIDs) and verifiable claims implemented on a distributed ledger technology present a powerful candidate to address this challenge. Thanks to Blockchain’s use of cryptographic identifiers, record immutability, and provenance, and the features provided by the DIDs and verifiable claims that enable identity management decentralization, portability, and discoverability. This article proposes a smartphone anticounterfeiting system based on an integrated approach of the technologies mentioned above. The proposed system eliminates the need for a central authority and provides the features of identity creation, transfer of ownership, and the capability of fast and secure reporting of stolen devices.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".