Preserving Privacy in Mobile Health Systems Using Non-Interactive Zero-Knowledge Proof and Blockchain
Bibliographic record
Abstract
The advent of miniaturized mobile devices with wireless communication capability and integrated with biosensors has revolutionized healthcare systems. The devices can be used by individuals as wearable accessories to collect health data regularly. This type of medical assistance supported by mobile devices to monitor patients and offer health services remotely is known as mobile health (mHealth). Although mHealth provides many benefits and has become popular, it can pose severe privacy risks. Many features in mHealth are managed through a smartphone. Thus, one of the most worrying issues involves communication between the monitoring devices and the smartphone. When communication uses Bluetooth, it is standard for a device to be paired with the smartphone; but generally, it is not exclusively associated with a specific mHealth app. This characteristic can allow a data theft attack by a malicious app or fake data injection by an illegitimate device. To address this issue, we present an authentication scheme based on Non-Interactive Zero-Knowledge Proof that is lightweight enough to run on mHealth devices with minimal resources. Our scheme ensures that legitimate devices interact exclusively with the official mHealth application. To ensure the patient's privacy-preserving throughout the system, we address the issues of storing, managing, and sharing data using blockchain. Since there is no privacy in the standard blockchain, we present a scheme in which the health data transmitted, stored, or shared are protected by Attribute-Based Encryption. The outcome is a system with fine-grained access control, entirely managed by the patient, and an end-to-end privacy guarantee.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".