Verifiable and Secure SVM Classification for Cloud-Based Health Monitoring Services
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
In cloud-based health monitoring services, support vector machine (SVM) classification techniques are often utilized by medical institutes to build medical decision models, which can be outsourced to a cloud server for producing medical decisions based on medical features from remote clients. In this article, we propose a verifiable and secure SVM classification scheme ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathsf {VSSVMC}$ </tex-math></inline-formula> ) for cloud-based health monitoring services in a malicious setting, where the cloud server may return invalid decisions. By constructing verifiable indices, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathsf {VSSVMC}$ </tex-math></inline-formula> ensures the verifiability of medical decisions, which enables clients to detect whether the cloud server returns incorrect or incomplete medical decisions. Symmetric key encryption is leveraged to ensure the confidentiality of the medical decision model and medical data with computational efficiency. We give security and verifiability definitions and provide formal security and verifiability proofs for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathsf {VSSVMC}$ </tex-math></inline-formula> . Performance analyses show that <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathsf {VSSVMC}$ </tex-math></inline-formula> is extremely efficient in terms of computation, communication, and storage. Experimental evaluations demonstrate that <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathsf {VSSVMC}$ </tex-math></inline-formula> achieves microsecond-level execution time with kilobyte-level communication and storage overheads on the tested data set.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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