Privacy Preserving Location based Services Through K-Anonymized Vehicular Social Network
Bibliographic record
Abstract
Location-based services (LBS) are the services that are used through users' mobile devices and provide them the information regarding nearby restaurants, hospitals, gas stations, shopping malls, cinema (to name a few). While using LBS, a user needs to provide his/her location coordinates (geo-coordinates) to the LBS server. The revelation of a user's location may seriously jeopardize his/her privacy. A common solution to this problem is the use of an intermediate anonymizer server that obfuscate the real location of a user among k other users. However, in this scenario, the anonymizer server must be a trusted and therefore, inherits the trust related issue and may also become a single point of failure. Moreover, if anonymizer compromises then the privacy of all the users is compromised. A vehicular adhoc network (VSN) is a subset of a mobile adhoc network (MANET) with some unique characteristics such as rapid speed of nodes (vehicles) that quickly change topologies. A vehicular social network is a combination of VANET and online social network. A VSN is formed by likeminded drivers/passengers or by common location. Like mobile nodes, the vehicles also utilize LBS services. However, an anonymizer cannot be used in this scenario because it needs to be updated with current locations of vehicles and therefore, jeopardizes the privacy of vehicles. A VSN can significantly help in this scenario. This research proposes a distributed k-anonymity based scheme that considers the social ties between the users of VSN and enable vehicles to use LBS in a privacy preserving manner. The proposed scheme uses a trusted authority (TA) such as a government law enforcement agency, that registers vehicles. However, the TA does not know what services are requested by a vehicle. On the other hand, the LBS server does not know which vehicle is requesting the service. In the end, the computational and communication overhead of the scheme is presented. The low computational overhead of TA, LBS server and vehicles shows that LBS can serve several vehicles in a very short time. Similarly, the TA can register and provide pseudo-identities to a number of vehicles without any significant delays.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".