Beyond Smart Homes: An In-Depth Analysis of Smart Aging Care System Security
Bibliographic record
Abstract
The upward trend in the percentage of the population older than 65 has made smart aging more relevant than ever before. Growing old in a traditional assisted living facility can take a toll on the mental well-being of the elderly individual, on top of other factors like extravagant costs, potential negligence from caregivers, and a ceaseless demand for healthcare personnel. Aging in one’s own space instead of a senior residence is the desirable alternative thanks to enabling technologies like the Internet of Things (IoT). The IoT facilitates connected healthcare, safety, entertainment, and social well-being of the older population. However, it suffers from a multitude of security vulnerabilities. Although researchers have investigated the security challenges of several IoT ecosystems, IoT systems in the context of smart aging care have not been well studied from a security perspective. In this article, we present an in-depth analysis of smart aging care system security issues. A smart aging care system is essentially a superset of smart homes and healthcare monitoring systems. The sheer variety of technologies at play and the amount of data generated, combined with physical vulnerabilities and a lack of technological exposure of the intended occupant group put smart aging care systems at great risk. Attacks against relatively benign smart home devices can bring serious consequences because of the context in which these devices are employed. Thus, the purpose of our study is four-fold: (i) defining the components and functionalities of a smart aging care system, (ii) identifying security vulnerabilities and outlining suitable countermeasures for them, (iii) analyzing how the attacks uniquely impact senior users’ Quality of Life (QoL), (iv) highlighting avenues for future research and how the threat landscape in smart aging care systems differ from general smart homes.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.011 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".