PCP: A Privacy-Preserving Content-Based Publish–Subscribe Scheme With Differential Privacy in Fog Computing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.130 | 0.146 |
| Research integrity | 0.000 | 0.001 |
| 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 it