PRISM: PRivacy-Aware Interest Sharing and Matching in Mobile Social Networks
Bibliographic record
Abstract
In a profile matchmaking application of mobile social networks, users need to reveal their interests to each other in order to find the common interests. A malicious user may harm a user by knowing his personal information. Therefore, mutual interests need to be found in a privacy preserving manner. In this paper, we propose an efficient privacy protection and interests sharing protocol referred to as PRivacy-aware Interest Sharing and Matching (PRISM). PRISM enables users to discover mutual interests without revealing their interests. Unlike existing approaches, PRISM does not require revealing the interests to a trusted server. Moreover, the protocol considers attacking scenarios that have not been addressed previously and provides an efficient solution. The inherent mechanism reveals any cheating attempt by a malicious user. PRISM also proposes the procedure to eliminate Sybil attacks. We analyze the security of PRISM against both passive and active attacks. Through implementation, we also present a detailed analysis of the performance of PRISM and compare it with existing approaches. The results show the effectiveness of PRISM without any significant performance degradation.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.027 | 0.085 |
| 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".