When Open-Source Information Backfires: Satellite Imagery and Privacy Breaches
Bibliographic record
Abstract
Open-source intelligence increasingly relies on new technologies to collect, process, and analyze open-source information. The enhancement of satellite imagery capabilities aligns with this goal, providing valuable data from hidden areas that are not easily recognizable. Giving more room to the private sector to invest and innovate in the satellite imaging industry results in remarkable achievements in the size of satellites, the quality of images, pricing, and accessibility of data. High- resolution images and potential live videos of the Earth can foster non-state open-source investigations, resulting in a multiplicity of narratives, where public interest exists. Nonetheless, privacy concerns should not be overshadowed by technological developments. The possible clashes between privacy and satellite imagery might be exacerbated if high-resolution images become widespread and the number of commercial satellite operators multiplies in territories with varying privacy laws. This Article considers privacy laws in Australia, the European Union, and the United States to examine to what extent these legal systems can minimize privacy breaches. It is contended that reasonable expectations of privacy can be an effective test to curb the publication of images infringing on individual privacy.<br/>
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".