scholarly journals PROS: A Privacy-Preserving Route-Sharing Service via Vehicular Fog Computing

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 66188-66197 ◽  
Author(s):  
Meng Li ◽  
Liehuang Zhu ◽  
Zijian Zhang ◽  
Xiaojiang Du ◽  
Mohsen Guizani
2019 ◽  
Vol 90 ◽  
pp. 158-174 ◽  
Author(s):  
Chunhui Piao ◽  
Yajuan Shi ◽  
Jiaqi Yan ◽  
Changyou Zhang ◽  
Liping Liu

2020 ◽  
Vol 26 ◽  
pp. 100265 ◽  
Author(s):  
Muhammad Arif ◽  
Guojun Wang ◽  
Valentina Emilia Balas ◽  
Oana Geman ◽  
Aniello Castiglione ◽  
...  

2020 ◽  
Vol 11 (1) ◽  
pp. 247-257 ◽  
Author(s):  
Jia-Nan Liu ◽  
Jian Weng ◽  
Anjia Yang ◽  
Yizhao Chen ◽  
Xiaodong Lin

IEEE Access ◽  
2017 ◽  
Vol 5 ◽  
pp. 3302-3312 ◽  
Author(s):  
Rongxing Lu ◽  
Kevin Heung ◽  
Arash Habibi Lashkari ◽  
Ali A. Ghorbani

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2659 ◽  
Author(s):  
Yinghui Zhang ◽  
Jiangfan Zhao ◽  
Dong Zheng ◽  
Kaixin Deng ◽  
Fangyuan Ren ◽  
...  

As an extension of cloud computing, fog computing has received more attention in recent years. It can solve problems such as high latency, lack of support for mobility and location awareness in cloud computing. In the Internet of Things (IoT), a series of IoT devices can be connected to the fog nodes that assist a cloud service center to store and process a part of data in advance. Not only can it reduce the pressure of processing data, but also improve the real-time and service quality. However, data processing at fog nodes suffers from many challenging issues, such as false data injection attacks, data modification attacks, and IoT devices’ privacy violation. In this paper, based on the Paillier homomorphic encryption scheme, we use blinding factors to design a privacy-preserving data aggregation scheme in fog computing. No matter whether the fog node and the cloud control center are honest or not, the proposed scheme ensures that the injection data is from legal IoT devices and is not modified and leaked. The proposed scheme also has fault tolerance, which means that the collection of data from other devices will not be affected even if certain fog devices fail to work. In addition, security analysis and performance evaluation indicate the proposed scheme is secure and efficient.


2021 ◽  
Author(s):  
Arwa Alrawais ◽  
Fatemah Alharbi ◽  
Moteeb Almoteri ◽  
Sara A Aljwair ◽  
Sara SAljwair

The COVID-19 pandemic has swapped the world, causing enormous cases, which led to high mortality rates across the globe. Internet of Things (IoT) based social distancing techniques and many current and emerging technologies have contributed to the fight against the spread of pandemics and reduce the number of positive cases. These technologies generate massive data, which will pose a significant threat to data owners’ privacy by revealing their lifestyle and personal information since that data is stored and managed by a third party like a cloud. This paper provides a new privacy-preserving scheme based on anonymization using an improved slicing technique and implying distributed fog computing. Our implementation shows that the proposed approach ensures data privacy against a third party intending to violate it for any purpose. Furthermore, our results illustrate our scheme’s efficiency and effectiveness.


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