Analysis on homomorphic technique for data security in fog computing
Bibliographic record
Abstract
Abstract The fog computing model has given new trends of networking with different types of devices providing services at the end‐user point. It inherits most advanced features of cloud computing with localization rather than centralization like a cloud. It builds a platform for the Internet of things of different standards. Similar to the cloud, it is prone to privacy and security threat while sharing resources and services at the network edge. When the request for support is with more sensitive data, for example, as in business or research area, then fog devices face many potential threats resulting in leakage of data. The existing cloud environment provides data sharing service to the legal requestor in a highly secured manner using cryptographic encryption techniques. The uploaded data undergoes encryption and decryption, at the sending and receiving end only on providing the private key. Even more, security can be achieved by further computation on encrypted data. As an extension to fog computing, the data communication between fog nodes‐fog nodes and fog node‐cloud center is done with encryption/decryption for ensuring confidentiality. Homomorphic encryption is a cryptographic technique which allows performing computations on encrypted data without decryption so that the original message need not be disclosed to intermediates (servers) who are the only service provider and not a data user. Our work is motivated by the flaw of security issues in fog computing platforms, which involve heterogeneous devices.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".