Achieving Privacy-Preserving Subset Aggregation in Fog-Enhanced IoT
Bibliographic record
Abstract
Fog-enhanced IoT (Internet of Things) is a fast-growing technology in which many firms and industries are currently investing to develop their own real-time and low latency scenarios. Compared with the traditional IoT, fog-enhanced IoT can offer a higher level of efficiency and stronger security by providing local data pre-processing, filtering, and forwarding mechanisms. However, fog-enhanced IoT faces some security and privacy challenges, since fog nodes are deployed at the network edge and may not be fully trustable. In this paper, we present a new privacy-preserving subset aggregation scheme, called PPSA, in fog-enhanced IoT scenarios, that enables a query user to gain the sum of data from a subset of IoT devices. To identify the subset, inner product similarity of the normalized vectors in the query user side and each IoT device is securely computed. If the inner product is greater than the user's specified threshold, IoT device's data will be privately aggregated to form the final response. To successfully launch privacy-preserving subset aggregation in the proposed scheme, we employ the Paillier homomorphic encryption to encrypt user's attribute vector, similarity threshold, IoT end-devices' data, as well as the intermediate results. To the best of our knowledge, this work is the first one to address the privacy-preserving subset aggregation in fog-enhanced IoT. We analyze and extensively evaluate the efficiency and security of the proposed PPSA scheme, and the detailed analysis and results indicate that our proposed PPSA scheme can practically achieve privacy-preserving subset aggregation with significant communication and computational cost saving.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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 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".