A Localized Bloom Filter-Based CP-ABE in Smart Healthcare
Bibliographic record
Abstract
Wearable technology-supported cloud-based smart health (s-health) has emerged as a promising answer to increase the efficiency and quality of healthcare as a result of rapid improvements in Internet of Things (IoT) technologies. However, the issues of data security and privacy preservation have not been fully resolved. In recent years, ciphertext policy attribute-based encryption (CP-ABE), which was developed as a versatile and potent cryptographic fundamental to accomplish one-to-many encryption with fine-grained access control, has been seen as a viable answer to the security issue in the cloud. The attribute values in the access policy, however, are supplied in cleartext in standard CP-ABE. This will conveniently reveal the data owners’ privacy (patients). Because the Internet of Things (IoT) in healthcare stores sensitive data in the cloud, security is crucial. The data must always be accessed via an access key when using traditional encryption techniques. Though the data cannot be accessed right away in an emergency, this offers greater security. The healthcare IoT created the break-glass concept to address this. The encryption technique is integrated with the broken glass idea to offer data protection and simple access in emergency scenarios. The majority of research papers employ cypher text policy attribute-based encryption (CP-ABE) with the broken glass idea to secure electronic health records. For improving data accessibility in the smart healthcare environment, modified cypher text policy attribute-based encryption (MCP-ABE) with the broken glass (BG) technique is suggested. Greater information security is achieved with this method, but the access policy is also dependent on keys that are vulnerable to hacking. To analyze the access policy individually throughout the key generation process, the attribute-based encryption procedure in this case uses the bloom filter. Information about the access policy is kept intact, which enhances the security of the keys. To continue serving patients and saving their lives, this modified CP-ABE is integrated with break glass in the smart healthcare facility. The experimental results demonstrated that, when compared to the lightweight break-glass procedure, the proposed solution is likewise the best in terms of decreased overhead. The main benefit of this strategy is that it uses the bloom filter concept in the MCP-ABE process, which protects the access policy attributes, to ensure that the key is never compromised. For data access in smart healthcare to preserve patients’ lives, the proposed MCP-ABE with broken glass is best.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".