Data Security in the Cloud Using pTree-based Homomorphic Intrinsic Data Encryption System (pHIDES)
Bibliographic record
Abstract
Cloud usage for storing data and performing operations has gained immense popularity in recent times. However, there are concerns that uploading data to the cloud increases the chances of unauthorized parties accessing it. One way to secure data from unauthorized access is to encrypt it. Even if the data is hacked, the hackers will not be able to retrieve any information from the data without knowing the 'Key' to decrypt it. But when data needs to be used for services such as data analytics, it must be in its original, non-encrypted form. Decrypting the data makes it vulnerable again, which is why Homomorphic Encryption could be the solution to this problem. In this encryption method, the analytical engine can use the encrypted data to perform analysis, where the analysis result will also be in decrypted form. Only authorized users can access the results using the 'Key.' This research proposal proposes a method called pHIDES to enhance data security in the cloud. The pHIDES (pTree-based Homomorphic Intrinsic Data Encryption System) represents data in pTree (Predicate tree) format, a data mining-ready data structure proven to manipulate a large volume of data effectively. The concept of Homomorphic Encryption (HME) along with pHIDES is discussed in our research, along with the algorithmic execution to analyze the effectiveness of the algorithm used to encrypt data in the cloud.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".