A privacy-preserving proximity friend notification scheme with opportunistic networking
Bibliographic record
Abstract
Recently, smartphones have revolutionized mobile and pervasive computing around the world, and many smartphone-based applications have been developed to enrich our daily lives, such as location-based application which offers various useful services to its users based on users' current locations like Google Latitude. However, the attractive features of smartphone-based applications inevitably incur higher risks for abuse if such applications and services do not take security and privacy consideration into account prior to it being widely deployment. In this paper, to simultaneously find the proximity friends and protect smartphone users' identity privacy, we utilize the opportunistic networking to propose an efficient privacy-preserving proximity friend notification (PFN) scheme. Specifically, by combining the Bluetooth and 3G techniques of smartphones, a smartphone user can first send his privacy-preserving friend notification packet in a physical proximity area, then once a friend nearby receives and identifies the packet with opportunistic networking, the friend can directly phone back to the user. Detailed security analysis with provable security technique demonstrates the security of the proposed PFN scheme. In addition, extensive simulations have also been conducted to examine its effectiveness in terms of friend notification delay.
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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 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".