A Fog-Assisted Privacy-Preserving Task Allocation in Crowdsourcing

Zhang, Jianhong; Zhang, Qijia; Ji, Shenglong

doi:10.1109/jiot.2020.2989578

Cited by 23 publications

(5 citation statements)

References 26 publications

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“…Theoretical analysis and experiments were performed to show that the approach could provide a strict privacy guarantee and enhance the performance. Zhang [43] aimed to address the issue of privacy-preserving crowdsourcing. They proposed an approach to managing the workers' computational requirements by allocating parts of the computation to the fog node.…”

Section: Privacy In Crowd-iotmentioning

confidence: 99%

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Towards Crowdsourcing Internet of Things (Crowd-IoT): Architectures, Security and Applications

2022

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Crowdsourcing can play an important role in the Internet of Things (IoT) applications for information sensing and gathering where the participants are equipped with geolocated devices. Mobile crowdsourcing can be seen as a new paradigm contributing to the development of the IoT. They can be merged to form a new and essential platform in crowdsourcing IoT paradigm for data collection from different sources and communication mediums. This paper presents a comprehensive survey for this new Crowdsourcing IoT paradigm from four different perspectives: (1) Architectures for Crowd-IoT; (2) Trustworthy, Privacy and Security for Crowd-IoT; (3) Resources, Sharing, Storage and Energy Considerations for Crowd-IoT; and (4) Applications for Crowd-IoT. This survey paper aims to increase awareness and encourage continuing developments and innovations from the research community and industry towards the Crowdsourcing IoT paradigm.

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Section: Privacy In Crowd-iotmentioning

confidence: 99%

“…Their approach achieves privacy protection of task content and interest keywords and resists the attacks of the workers. Zhang [43] aimed to address the issue of privacy-preserving crowdsourcing. They proposed an approach to managing the workers' computational requirements by allocating parts of the computation to the fog node.…”

Section: Privacy In Crowd-iotmentioning

confidence: 99%

Towards Crowdsourcing Internet of Things (Crowd-IoT): Architectures, Security and Applications

2022

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“…Usually, the data uploaded in the FSs include trajectories, unique identifiers, and location information which leads to potential financial loss or privacy leakage. Fog computing has advanced services which may be invalidated when misled of prediction and optimization results occur due to inappropriate global aggregation result 16 …”

Section: Introductionmentioning

confidence: 99%

“…Fog computing has advanced services which may be invalidated when misled of prediction and optimization results occur due to inappropriate global aggregation result. 16 To overcome issues related to data centralization drawbacks in vehicular networks, researchers introduced FL as a solution. FL is used to enrich AI in order to apply computation applications into end devices to protect privacy issues.…”

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confidence: 99%

An integrated security approach for vehicular networks in smart cities

Muthukumaran

Thirunnavukkarasan

Amanullah³

et al. 2023

Trans Emerging Tel Tech

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Fog computing, which is an extension of cloud computing is one of the cornerstone for Internet of Things, that witnessed rapid growth because of its ability to enhance several difficult problems such as network congestion, latency, and the lack of regional autonomy. However, privacy concerns and the resulting inefficiency are causing the performance of fog computing to suffer. While suffering from poisoning attacks, the vast majority of current works do not take into consideration of proper balance between them. Specifically, we present blockchain‐enabled secure federated learning in vehicle network (BSFLVN) system model for traffic flow prediction in urban computing to overcome the aforementioned difficulties and narrow the gap. When vehicle devices trade local learning updates, BSFLVN enables them to be exchanged with a blockchain‐based global learning model, which is confirmed by miners. To improve accuracy of classification we proposed federated learning‐based gated recurrent unit for local model update using FL‐GRNN algorithm and global model update using FL‐AGG algorithm. The BSFLVN, which is built on top of this, allows autonomous deep learning‐based GRU to take place without the need for a centralized authority to maintain the global model and coordinate by using the PoW consensus mechanism of blockchain. For preserving privacy and security of local and global model updates we employ the LDP mechanism. For the analysis of the latency performance of BSFLVN, as well as the derivation of the best block production rate we consider communication, consensus delays, and computing cost. The results of a thorough examination demonstrate that BSFLVN outperforms its competitors in terms of privacy protection, efficiency, and resistance to poisoning attacks, among other areas. Various deadline time iteration process is simulated for result evaluation using Fashion MINIST data in which over 93% of accuracy is obtained.

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“…Recently, FL is explored extensively in IoT applications [78][79][80][81][82]. Many studies focus on privacy-preserving crowdsourcing [107,108], and leveraging the fog computing or edge computing to improve the performance as they have gained popularity [109][110][111][112][113][114]. For example, Wu et al [107] proposed two generic models for quantifying mobile users' privacy and data utility in crowdsourced location-based services, respectively.…”

Section: Existing Studies On Federated Learning For Iotmentioning

confidence: 99%

Privacy-preserving data analytics

Zhao¹

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whose continuous guidance, support, and encouragement have been invaluable throughout this study. Under their close mentorship, I have successfully completed a number of publications. I have also come to know how to explore new research areas, choose research topics, build new approaches and do experiments. In particular, their enthusiasm and awareness of research have encouraged me to do better successes. Furthermore, I would like to express my thankfulness to my thesis advisory committee members Prof. NG Wee Keong, Asst Prof. Hung Dinh Nguyen, and Asst Prof. Han Yu, for their valuable comments on my research.

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A Fog-Assisted Privacy-Preserving Task Allocation in Crowdsourcing

Cited by 23 publications

References 26 publications

Towards Crowdsourcing Internet of Things (Crowd-IoT): Architectures, Security and Applications

Towards Crowdsourcing Internet of Things (Crowd-IoT): Architectures, Security and Applications

An integrated security approach for vehicular networks in smart cities

Privacy-preserving data analytics

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