DLP: Achieve Customizable Location Privacy With Deceptive Dummy Techniques in LBS Applications
Bibliographic record
Abstract
As a straightforward consequence of advances in the Internet of Things (IoT), location-based service (LBS) applications have been pervasive in our daily lives. Nevertheless, since those LBS applications will continuously collect and disclose users’ location data, major concerns on privacy leakage are raised. Aiming at the challenge, in this article, we first build up a detect module (DM) and employ it to investigate more than 80% of LBS applications are keen on tracking users. Then, to thwart the threats from those LBS applications, we exploit the deceptive dummy techniques and design a dummy-based location privacy preserving scheme, named dummy location provider (DLP), which comprises three algorithms, namely, Spread, Shift, and Switch. Specifically, Spread and Shift are in charge of generating deceptive dummies and trajectories. And with Switch, users’ real locations are replaced with dummy trajectories before being submitted to LBS applications. As a result, users can not only prevent applications from accessing location data arbitrarily, but also avoid being questioned by applications in terms of honesty. Furthermore, to guarantee necessary functions of LBS, DLP offers customizable privacy-preserving strategies for users, which can achieve flexible location data usage control. Finally, our DLP can also attain achievable and effortless deployment over smart devices. Detailed security analysis indicates that DLP resists inference attacks even facing skeptical applications. In addition, for performance evaluation, a DLP application (DLPA) is developed on the Android platform and tested in the real environment, and the extensive experimental results demonstrate that the DLPA is indeed effective and high efficiency in practice.
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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 | 0.002 |
| 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 | 0.002 |
| Open science | 0.012 | 0.009 |
| Research integrity | 0.000 | 0.001 |
| 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; both teacher heads agree on what is shown here.
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".