Data Protection in Internet of Medical Things Using Blockchain and Secret Sharing Method
Bibliographic record
Abstract
<title>Abstract</title> Internet of Medical Things (IoMT) combines Internet of Things (IoT)with medical devices to facilitate healthcare, providing affordable solutions, and faster treatments to patients. An increase in IoMT use hasled to several security/privacy challenges, since many IoMT devices havenot been designed with security and privacy features in mind, whichmakes them vulnerable to attacks. Furthermore, as security risks andthreats affect all the layers developed for IoMT-based architectures, anIoMT network must follow stricter privacy and security specificationscompared to other IoT devices. In order to address this, we present anew IoT-based architecture to improve the data privacy, security, andintegrity leveraging distributed InterPlanetary File System (IPFS) storage and blockchain. The data captured from medical devices is split intomultiple encrypted pieces using Secret Sharing Algorithm (SSA) andthese pieces are then stored in distributed IPFS storage hosted on edgeand cloud servers, and their copies are verifiable by a blockchain network. The applied SSA method ensures that even if a piece of data iscompromised, the original data is neither leaked nor lost. Our proposedarchitecture is implemented and tested on an IoMT-based monitoring system to investigate its feasibility, scalability, and performance.
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 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 | 0.014 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.005 | 0.029 |
| Research integrity | 0.001 | 0.003 |
| 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".