Balancing Privacy and Accuracy in IoT Using Domain-Specific Features for Time Series Classification
Bibliographic record
Abstract
$\varepsilon$-Differential Privacy (DP) has been popularly used for anonymizing data to protect sensitive information and for machine learning (ML) tasks. However, there is a tradeoff in balancing privacy and achieving ML accuracy since$\varepsilon\text{-DP}$reduces the model's accuracy. Moreover, not many studies have applied DP to time series from sensors and Internet-of- Things (IoT) devices. In this work, we try to achieve the accuracy of ML models trained with$\varepsilon\text{-DP}$data to be as close to the ML models trained with non-anonymized data for two different physiological time series. We propose to transform time series into domain-specific 2D (image) representations such as scalograms, recurrence plots (RP), and their joint representation as inputs for training classifiers. These images allow us to apply state-of-the-art image classifiers to obtain accuracy comparable to classifiers trained on non-anonymized data by exploiting the additional information such as textured patterns from these images. To achieve classifier performance with anonymized data close to non-anonymized data, it is important to identify the value of$\varepsilon$and the input feature. Experimental results demonstrate that the performance of the ML models with scalograms for one of the datasets and RP for the other dataset was comparable to ML models trained on their non-anonymized versions. Motivated by the promising results, our work suggests an end-to-end IoT ML edge-cloud architecture that employs our technique to train ML models on$\varepsilon\text{-DP}$physiological data. Our technique ensures the privacy of individuals while processing and analyzing the data at the edge securely and efficiently.
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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 | 0.008 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".