Data Security and Privacy-Preserving in Edge Computing Paradigm: Survey and Open Issues

Zhang, Jiale; Chen, Bing; Zhao, Yanchao; Cheng, Xiang; Hu, Feng

doi:10.1109/access.2018.2820162

Cited by 340 publications

(206 citation statements)

References 122 publications

Supporting

Mentioning

206

Contrasting

Order By: Relevance

“…It should be noted that our work aims to improve the algorithm efficiency, while the privacy protection of sensitive data is not taken into account. At present, data security has become a focus of attention in the field of artificial intelligence, and some solutions for data privacy protection in complex systems with edge computing have been proposed [41][42][43][44]. In our future work, how to improve the efficiency of quantum machine learning algorithms while ensuring the privacy protection of sensitive data, such as [45][46][47][48], will become our direction.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Quantum-Based Feature Selection for Multiclassification Problem in Complex Systems with Edge Computing

et al. 2020

View full text Add to dashboard Cite

The complex systems with edge computing require a huge amount of multifeature data to extract appropriate insights for their decision making, so it is important to find a feasible feature selection method to improve the computational efficiency and save the resource consumption. In this paper, a quantum-based feature selection algorithm for the multiclassification problem, namely, QReliefF, is proposed, which can effectively reduce the complexity of algorithm and improve its computational efficiency. First, all features of each sample are encoded into a quantum state by performing operations CMP and Ry, and then the amplitude estimation is applied to calculate the similarity between any two quantum states (i.e., two samples). According to the similarities, the Grover–Long method is utilized to find the nearest k neighbor samples, and then the weight vector is updated. After a certain number of iterations through the above process, the desired features can be selected with regards to the final weight vector and the threshold τ. Compared with the classical ReliefF algorithm, our algorithm reduces the complexity of similarity calculation from O(MN) to O(M), the complexity of finding the nearest neighbor from O(M) to OM, and resource consumption from O(MN) to O(MlogN). Meanwhile, compared with the quantum Relief algorithm, our algorithm is superior in finding the nearest neighbor, reducing the complexity from O(M) to OM. Finally, in order to verify the feasibility of our algorithm, a simulation experiment based on Rigetti with a simple example is performed.

show abstract

Section: Conclusion and Discussionmentioning

confidence: 99%

Quantum-Based Feature Selection for Multiclassification Problem in Complex Systems with Edge Computing

et al. 2020

View full text Add to dashboard Cite

show abstract

“…(3) Edge computing can utilize idle resources effectively, which helps further reduce the load of central processing. (4) Edge computing enhances the offline protection of privacy and security [27]. Although a cloud-based solution may also help offload the computation overhead of security protection, users are required to hand in their security keys to cloud servers.…”

Section: Edge Computingmentioning

confidence: 99%

A Novel Multi-Agent-Based Collaborative Virtual Manufacturing Environment Integrated with Edge Computing Technique

et al. 2019

View full text Add to dashboard Cite

This paper proposes a multi-agent-based collaborative virtual manufacturing environment (VME) to save energy consumption and improve efficiency in the manufacturing process. In order to achieve the high autonomy of the manufacturing system, a multi-agent system (MAS) is designed to build a collaborative VME. In this new VME environment, edge computing is embedded to strengthen the cyber resource utilization and system economy. Moreover, an efficient communication channel between networks is proposed. The subsequent cooperation and collaboration protocols among agents are designed to ensure flexible and process-oriented operations. Furthermore, the fuzzy resolution algorithm is employed to resolve the competition conflicts among function-similar MASs in the distributed manufacturing scenario. Lastly, a simulation and case study are performed to evaluate the performance of the proposed VME in Internet of Things (IoT)-based manufacturing. The analysis results have demonstrated the feasibility and effectiveness of the proposed VME system.

show abstract

“…Lu et al 5 proposed the LDPA scheme, which can aggregate heterogeneous data on the Internet of things (IoT) applications. In the cloud environment, privacy leakage is one of the major threats because the data of users (including some sensitive personal information) have shifted gradually from their mobile devices to cloud servers 6 . In IoT environments, privacy issues are more severe since there are a great number of users, edge servers, and potential adversaries.…”

Section: Introductionmentioning

confidence: 99%

Lightweight edge‐based kNN privacy‐preserving classification scheme in cloud computing circumstance

Tan

Liu

et al. 2020

Concurrency and Computation

View full text Add to dashboard Cite

Summary Because mobile terminals have limited computing and storage resources, individuals tend to outsource their data generated from mobile devices to clouds to do data operations. However, utilization of the abundant computation and storage resources of clouds may pose a threat to user's private data. In this paper, we focus on the issue of encrypted k‐nearest neighbor (kNN) classification on the cloud. In the past few years, many solutions were proposed to protect the user's privacy and data security. Unfortunately, most privacy‐preserving data mining schemes are not lightweight, which are not practical in real‐world applications. To solve this issue, we proposed a lightweight edge‐based kNN (EBkNN) classification scheme over encrypted cloud database utilizing edge computing technology. Our proposed scheme can provide several security guarantees: (i) user's data security, (ii) user's query privacy, and (iii) data access patterns. We analyzed the security of our scheme utilizing the semi‐honest security model and evaluated the performance using a synthetic dataset. The experiment results indicate that our scheme is more lightweight than the state‐of‐the‐art scheme.

show abstract

Data Security and Privacy-Preserving in Edge Computing Paradigm: Survey and Open Issues

Cited by 340 publications

References 122 publications

Quantum-Based Feature Selection for Multiclassification Problem in Complex Systems with Edge Computing

Quantum-Based Feature Selection for Multiclassification Problem in Complex Systems with Edge Computing

A Novel Multi-Agent-Based Collaborative Virtual Manufacturing Environment Integrated with Edge Computing Technique

Lightweight edge‐based kNN privacy‐preserving classification scheme in cloud computing circumstance

Contact Info

Product

Resources

About