Evaluating Holistic Privacy Risk Posed by Smart Home Ecosystem: A Capability-Oriented Model Accommodating Epistemic Uncertainty and Wisdom of Crowds
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
Evaluating the holistic privacy risk (HPR) presented by a smart home ecosystem (SHE), encompassing both internal and external entities that may be targeted by different adversaries seeking to compromise users’ privacy, can enhance the comprehensive understanding of the privacy risk landscape within the SHE. This matter is influenced by the complexity of risk surroundings, the diverse perspectives of users toward privacy, and the lack of historical data. Unfortunately, existing literature falls short in addressing these factors. To fill the gap, this article develops an innovative capability-oriented model that accommodates epistemic uncertainty and wisdom of crowds (WoC), designed to assist smart home device manufacturers in accurately assessing HPR posed by their SHEs. The model presents a method for representing subjective judgments that captures epistemic uncertainty and a technique for weighting individual judgments to mitigate overconfidence bias, thus effectively harnessing WoC. In addition, this model features two specialized methods: one for quantifying HPR and another for prioritizing associated single risks, both tailored to operate effectively within uncertain context. These innovative methods are versatile and can be applied to various risk assessment scenarios, especially where historical data are not available. The practicality and effectiveness of our model are demonstrated through a detailed case study.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.004 | 0.001 |
| 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