Toward a Privacy-Preserving and Secure Smart City: Recent Advances in User-Centric Applications
Bibliographic record
Abstract
The ever-increasing global population has caused rapid urbanization in recent decades. Many cities around the world are trying to leverage information and communication technologies to resolve the resulting problems, such as traffic congestion and high energy consumption. This trend is part of the movement towards the development of the so-called smart cities that provide efficient, comfortable, and happy lives to their residents. In this respect, smart cities are indeed promising but have significant underlying complexity due to the number of variety of domains involved, including living, economy, mobility, governance, etc. However, in recent years, numerous cyber attacks and privacy leaks have been major obstacles to the widespread adoption and deployment of smart city applications. In general, smart city domains can be classified into user-centric applications and non-user-centric applications, such as critical infrastructures. This paper examines the key security and privacy challenges associated with recently trending user-centric smart city applications. First, we study the leading technologies along with superior security methods and privacy-enhancing technologies in smart cities. We also provide a critical survey of the security and privacy of novel user-centric smart city applications, namely smart parking, smart charging, and smart home. Our survey provides a detailed review of recent, relevant, and state-of-the-art research works to assist readers in gaining a comprehensive understanding of security and privacy challenges associated with smart cities. Finally, it outlines some research directions worth investigating in the future. The ultimate goal of this survey is to shed light on the pressing security and privacy challenges in smart cities and to provide insights for the development of secure and privacy-preserving smart cities.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.014 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".