Enhancing Privacy Preservation of IoT-Based Smart City Using Software-Defined Networking and Differential Privacy Distributions
Bibliographic record
Abstract
Internet of Things (IoT) aims at connecting all objects through the Internet. The IoTbased smart city is the concept of employing IoT devices to manage cities more easily, quickly and effectively. Recently, a new paradigm has emerged in the networking world, called Software-defined Networking (SDN), which separates the control data plane and the data plane. On the other hand, IoT devices embedded in a smart city often detect sensitive data. It is of paramount importance to prevent leaking this data unintentionally, known as privacy-preserving. Although there are some methods to preserve data privacy in smart cities, they are either expensive or cannot preserve privacy efficiently. Intending to provide an efficient privacy-preserving in a cost-effective smart city, we propose a novel method called Differential Privacy-preserving Smart City (DPSmartCity). It maintains the privacy of IoT devices sensitive data by equipping the environment with SDN technologies and leveraging a customized Differential Privacy (DP) technique while its distribution changes frequently. We then mathematically investigate the DP aspect of the given algorithms by providing several proofs. The evaluation results show the effectiveness of the proposed method for diffident parameters, such as overload amount and latency points. Based on our threat model, it is 25.89% more robust against unintentional disclosure of sensitive data from a penetration point of view.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".