Privacy-Preserving Pedestrian Detection for Smart City with Edge Computing

Yuan, Danni; Zhu, Xiaoyan; Mao, Yaoru; Zheng, Binwen; Wu, Tao

doi:10.1109/wcsp.2019.8927923

Cited by 5 publications

(2 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Compared with cloud computing, it can avoid the delay caused by the large amount of data uploaded to the server. In some specific application scenarios in the field of public security, the use of edge mobile devices can better meet the requirements of application scenarios, such as realtime detection of face information and alerting the police to the criminal records of dangerous persons encountered [34]. In addition, Dany-yuan found that 89% of Wireless-Fidelity (Wi-Fi) hotspots are unsafe, and massive personal privacy data are included in the real-time transmission of large amounts of data.…”

Section: Implementation Of a Safety Supervision Algorithmmentioning

confidence: 99%

See 1 more Smart Citation

Highly secure edge-intelligent electric motorcycle management system for elevators

et al. 2020

View full text Add to dashboard Cite

Because of their portability, electric motorcycles are usually pushed into elevators by residents and charged in the home, which has serious safety risks. Traditional manual-based methods to manage this behavior have poor monitoring effects and high costs. As for automatic management systems using artificial intelligence (AI), the deployment method matters. Cloud-based deployment methods have the disadvantages of high latency, high risk of privacy leakage, and heavy network transmission loads. In this paper, we propose a highly secure edge-intelligent electric motorcycle management system for elevators. By using edge-based deployment method, the monitor pictures are processed locally without being uploaded to the cloud, which can effectively resist network attacks and prevent residents' private data from being leaked. To improve the system security, we fully analyze the challenges faced in the application scenarios and introduce security threat identification (STI-1H8) model to identify the security threats. In addition, we propose several data enhancement methods to improve the system recognition accuracy. Experimental results show that our system can achieve a high recall rate of 0.82. By using data enhancement and data mixing strategies, it can reduce the misjudgment rate by 0.35. Moreover, compared to cloud computing, our edge-based method can reduce the latency by 19.6%, meeting real-time requirements.

show abstract

Section: Implementation Of a Safety Supervision Algorithmmentioning

confidence: 99%

“…Compared with cloud computing, edge computing shows excellent performance in reducing communication delays [6,7], alleviating transmission loads [8] and preventing user privacy leakages [9,10]. However, there still exist several security threats.…”

Section: Introductionmentioning

confidence: 99%