PCP: A Privacy-Preserving Content-Based Publish–Subscribe Scheme With Differential Privacy in Fog Computing
Bibliographic record
Abstract
Fog computing dramatically extends the cloud computing to the edge of the network and admirably solves the problem that the brokers (in publish-subscribe system) generally lack of computing capacity and energy power. However, brokers may be disguised, hacked, sniffed, and corrupted. The traditional security technology cannot protect the system privacy when facing a possible collusion attack. In this paper, we propose a privacy-preserving content-based publish/subscribe scheme with differential privacy in fog computing context, named PCP, where the fog nodes act as the brokers. Specifically, PCP firstly utilizes the U-Apriori algorithm to mine the top-K frequent itemsets (i.e., the attributes) from uncertain data sets, then applies the exponential and Laplace mechanism to ensure the differential privacy, and the broker uses the mined top-K itemsets to match appropriate publisher and subscriber finally. Security analysis shows that the PCP can guarantee differential privacy in theory. To evaluate the performance of PCP, we carry out experiments with real-world scenario data sets. The experimental results show that PCP efficiently achieves the tradeoff between the system cost and the privacy demand.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".