Privacy-preserving matchmaking For mobile social networking secure against malicious users
Bibliographic record
Abstract
The success of online social networking and of mobile phone services has resulted in increased attention to mobile social networking. Matchmaking is a key component of mobile social networking. It notifies users of nearby people who fulfil some criteria, such as having shared interests, and who are therefore good candidates for being added to a user's social network. Unfortunately, the existing matchmaking approaches are troublesome from a privacy point of view. One approach has users' smartphones broadcast their owners' personal information to nearby devices. This approach reveals more personal information than necessary. The other approach requires a trusted server that participates in each matchmaking operation. Namely, the server knows the interests and current location of each user and performs matchmaking based on this information. This approach allows the server to track users. This paper proposes a privacy-preserving matchmaking protocol for mobile social networking that lets a potentially malicious user learn only the interests (or some other traits) that he has in common with a nearby user, but no other interests. In addition, the protocol is distributed and does not require a trusted server that can track users or that needs to be involved in each matchmaking operation. We present an implementation and evaluation of our protocol on Nexus One smartphones and demonstrate that the protocol is practical.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| 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".