Smart Phone Anti-counterfeiting System Using a Decentralized Identity Management Framework
Bibliographic record
Abstract
The effect of counterfeiting on smart phone sales worldwide is estimated at 184 million units, valued at 45.3 billion EUR or 12.9 % of total sales. The mobile phone counterfeiting, in addition to its economic impact, has serious security, privacy, and even general safety concerns. The proliferation of Smart Phones devices is on the rise, where the number of smart phone devices shipped in 2017 has surpassed 1.5 billion devices and it is has reached around 1.2 billion devices by end of Q3 2018. Most of those devices are attached to different mobile networks operated around the globe, and the challenges arising is how those devices' identities are maintained and verified in addition to how the supply chain actors in smart phones industry can ensure the access to device identity throughout the device life cycle with less control from third parties. Blockchain as a distributed ledger technology positions itself as a suitable candidate to address this challenge. That is mainly attributed to Blockchain's use of cryptographic identifiers, records immutability, and provenance. These features, together, provide a platform to implement the functions of smart phone identity management functions in a global and decentralized environment. This paper presents a the use of a decentralized identity management framework to implement a system for Smart Phone Anti-Counterfeiting that eliminates the need for a central authority and provides the features of identity creation and transfer of ownership, along with the capability of fast and secure reporting of stolen and lost devices that takes effect in the shortest time. The work is implemented using a set of solidity of smart contracts deployed on a private Ethereum Blockchain.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 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".