A Comprehensive Trustworthy Data Collection Approach in Sensor-Cloud Systems

Wang, Tian; Yang, Li; Fang, Weiwei; Xu, Wenzheng; Liang, Junbin; Chen, Yewang; Liu, Xuxun

doi:10.1109/tbdata.2018.2811501

Cited by 43 publications

(22 citation statements)

References 33 publications

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“…We further find 7 studies with or more than 30 citations. These studies are [26], [39], [45], [46], [53], [55], [58]. Generally, the overall number of citations for the selected studies is 675, and the average citations per paper is 16.46.…”

Section: ) Citation Impactmentioning

confidence: 99%

Data Collection in Sensor-Cloud: A Systematic Literature Review

Ali¹

2020

Preprint

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Section: ) Citation Impactmentioning

confidence: 99%

Data Collection in Sensor-Cloud: A Systematic Literature Review

Ali¹

2020

Preprint

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“…Q represents the FHMM algorithm, which decides the mapping between the observed state and switch state. 1) As the mapping between the observed state and switch state in the FHMM can be seen as a linear mapping, we have (D') D' Q k b  (12) () Q D kD b  (13) ' D D D    (14) k and b are linear parameters and  represent the correction coefficient.…”

Section: Proofmentioning

confidence: 99%

“…In fact, there is a significant body of work analyzing the users' privacy-preservation [9][10][11][12][13]. The major privacy-preserving solutions can be classified into homomorphic encryption [42] [43], flattening energy signatures by batterybased load hiding (BLH) [16][17][18][19][20][21] and noise addition [22][23][24].…”

Section: Introductionmentioning

confidence: 99%

Achieving differential privacy against non‐intrusive load monitoring in smart grid: A fog computing approach

Cao

Liu

et al. 2018

Concurrency and Computation

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Fog computing, a non-trivial extension of cloud computing to the edge of the network, has great advantage in providing services with a lower latency. In smart grid, the application of fog computing can greatly facilitate the collection of consumer's fine-grained energy consumption data, which can then be used to draw the load curve and develop a plan or model for power generation. However, such data may also reveal customer's daily activities. Non-intrusive load monitoring (NILM) can monitor an electrical circuit that powers a number of appliances switching on and off independently. If an adversary analyzes the meter readings together with the data measured by an NILM device, the customer's privacy will be disclosed. In this paper, we propose an effective privacy-preserving scheme for electric load monitoring, which can guarantee differential privacy of data disclosure in smart grid. In the proposed scheme, an energy consumption behavior model based on Factorial Hidden Markov Model (FHMM) is established. In addition, noise is added to the behavior parameter, which is different from the traditional methods that usually add noise to the energy consumption data. The analysis shows that the proposed scheme can get a better trade-off between utility and privacy compared with other popular methods.

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“…Moreover, when the data itself is insecure, which may include trust spoof attacks and replicated-sink attacks, the data collection needs to identify whether such data could be received. Wang et al [43] proposed a trust evaluation scheme to ensure the trustworthiness of sensors and mobile sinks, which protects the system from trust spoof attacks and replicated-sink attacks.…”

Section: Related Workmentioning

confidence: 99%

Energy-Efficient Cloudlet Management for Privacy Preservation in Wireless Metropolitan Area Networks

Huang

Dou

et al. 2018

Security and Communication Networks

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Currently, cloudlet based wireless metropolitan area network (WMAN) is emerging as an effective paradigm to extend the performance of mobile devices, which enables the execution of computational intensive mobile applications. But the normal operation of cloudlets consumes a large amount of energy, which brings about carbon dioxide emissions, aggravation of the greenhouse effect, etc. Meanwhile the data transmission of various mobile users among cloudlets would cause leakage of personal privacy. In view of this challenge, we propose an energy-efficient cloudlet management method, named ECM, for privacy preservation in WMAN. Technically, an optimization model is designed to formalize our problem. Then, based on live virtual machine (VM) migration technique, a corresponding privacy-aware VM scheduling method for energy saving is designed to determine which VMs should be migrated and where they should be migrated. Finally, experimental data demonstrate that the proposed method is both efficient and effective.

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A Comprehensive Trustworthy Data Collection Approach in Sensor-Cloud Systems

Cited by 43 publications

References 33 publications

Data Collection in Sensor-Cloud: A Systematic Literature Review

Data Collection in Sensor-Cloud: A Systematic Literature Review

Achieving differential privacy against non‐intrusive load monitoring in smart grid: A fog computing approach

Energy-Efficient Cloudlet Management for Privacy Preservation in Wireless Metropolitan Area Networks

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