SecureDL: A privacy preserving deep learning model for image recognition over cloud
Bibliographic record
Abstract
The key benefits of cloud services, such as low cost, access flexibility, and mobility, have attracted users worldwide to utilize the deep learning algorithms for developing computer vision tasks. Untrusted third parties maintain these cloud servers, and users are always concerned about sharing their confidential data with them. In this paper, we addressed these concerns for by developing SecureDL, a privacy-preserving image recognition model for encrypted data over cloud. Additionally, we proposed a block-based image encryption scheme to protect images' visual information. The scheme constitutes an order-preserving permutation ordered binary number system and pseudo-random matrices. The encryption scheme is proved to be secure in a probabilistic viewpoint and through various cryptographic attacks. Experiments are performed for several image recognition datasets, and the achieved recognition accuracy for encrypted data is close with non-encrypted data. SecureDL overcomes the storage, and computational overheads occurred in fully-homomorphic and multi-party computations based secure recognition schemes.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".