Trusted Base Stations-Based Privacy Preserving Technique in Location-Based Services
Bibliographic record
Abstract
With the advent in mobile and internet technologies, there is a significant increase in the number of users using smartphones and other internet based applications. There are a large number of applications available online that use the internet and provide useful information to the users. These include ones that provide location-based services e.g. google maps etc. These applications provide many facilities to the users who want information regarding a specific area or directions using an optimal path to a destination. Due to these reasons, the number of clients using these applications is increasing on a daily basis. Although these services are very useful and are making it easy for us to get information about our surroundings, some issues are also linked with the use of these applications and their services. One of the more significant issues of using these services is privacy with respect to sending personal location information to location-based services servers. Researchers have provided many solutions to solve these issues. One of the solutions is through caching and use of k-anonymity techniques. In this paper, we have proposed a method to solve the privacy issue that uses caching data approach to reduce the number of queries sent to the location-based services server. We also discuss the use of the concept of k-anonymity when no relevant data is available in cache, and queries are sent to the server.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.016 |
| Open science | 0.013 | 0.012 |
| 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; 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".