Analysis on homomorphic technique for data security in fog computing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.007 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it