The Price of Privacy: Quantifying the Impact on Localization Accuracy in Underwater Secure Localization
Bibliographic record
Abstract
A persistent risk in many underwater localization solutions is the leakage of position information, since anchor positions are revealed to the sensors performing position estimation. While cryptography-based secure localization mechanisms for underwater acoustic sensor networks (UASNs) can protect the privacy of anchors, varying levels of privacy preservation impact localization accuracy. Therefore, it is necessary to derive a quantitative relationship between privacy preservation levels and localization accuracy. In this paper, we first model the probability density function (PDF) of anchor decoding errors under privacy preservation constraints when anchors adopt an encrypted system to safeguard position information. The model parameters are optimized using the expectation-maximization (EM) algorithm. Next, based on the ranging PDF and anchor decoding error PDF distribution, we derive the position error bound (PEB) function that links privacy preservation levels with target localization accuracy. Simulation and field experiment results validate the effectiveness of the theoretical model.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 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".