Blockchain-Based Digital Contact Tracing Apps for COVID-19 Pandemic Management: Issues, Challenges, Solutions, and Future Directions
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
The COVID-19 pandemic has caused substantial global disturbance by affecting more than 42 million people (as of the end of October 2020). Since there is no medication or vaccine available, the only way to combat it is to minimize transmission. Digital contact tracing is an effective technique that can be utilized for this purpose, as it eliminates the manual contact tracing process and could help in identifying and isolating affected people. However, users are reluctant to share their location and contact details due to concerns related to the privacy and security of their personal information, which affects its implementation and extensive adoption. Blockchain technology has been applied in various domains and has been proven to be an effective approach for handling data transactions securely, which makes it an ideal choice for digital contact tracing apps. The properties of blockchain such as time stamping and immutability of data may facilitate the retrieval of accurate information on the trail of the virus in a transparent manner, while data encryption assures the integrity of the information being provided. Furthermore, the anonymity of the user's identity alleviates some of the risks related to privacy and confidentiality concerns. In this paper, we provide readers with a detailed discussion on the digital contact tracing mechanism and outline the apps developed so far to combat the COVID-19 pandemic. Moreover, we present the possible risks, issues, and challenges associated with the available contact tracing apps and analyze how the adoption of a blockchain-based decentralized network for handling the app could provide users with privacy-preserving contact tracing without compromising performance and efficiency.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Open science | 0.001 | 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