Efficient and Secure Outsourcing of Differentially Private Data Publishing With Multiple Evaluators

Li, Jin; Ye, Heng; Li, Tong; Wang, Wei; Lou, Wenjing; Hou, Thomas Y.; Liu, Jiqiang; Lu, Rongxing

doi:10.1109/tdsc.2020.3015886

Cited by 51 publications

(38 citation statements)

References 40 publications

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“…Finally, it is necessary to protect the security and privacy of big data. How to protect data security and privacy is an important research direction 47–49 …”

Section: Discussionmentioning

confidence: 99%

A long short‐term memory‐based model for greenhouse climate prediction

et al. 2021

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Greenhouses can grow many off‐season vegetables and fruits, which improves people's quality of life. Greenhouses can also help crops resist natural disasters and ensure the stable growth of crops. However, it is highly challenging to carefully control the greenhouse climate. Therefore, the proposal of a greenhouse climate prediction model provides a way to solve this challenge. We focus on the six climatic factors that affect crops growth, including temperature, humidity, illumination, carbon dioxide concentration, soil temperature and soil humidity, and propose a GCP_lstm model for greenhouse climate prediction. The climate change in greenhouse is nonlinear, so we use long short‐term memory (LSTM) model to capture the dependence between historical climate data. Moreover, the short‐term climate has a greater impact on the future trend of greenhouse climate change. Therefore, we added a 5‐min time sliding window through the analysis experiment. In addition, sensors sometimes collect wrong climate data. Based on the existence of abnormal data, our model still has good robustness. We experienced our method on the data sets of three vegetables: tomato, cucumber and pepper. The comparison shows that our method is better than other comparison models.

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“…Finally, it is necessary to protect the security and privacy of big data. How to protect data security and privacy is an important research direction 47–49 …”

Section: Discussionmentioning

confidence: 99%

A long short‐term memory‐based model for greenhouse climate prediction

et al. 2021

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“…Leung et al conducted a visual analysis of the spread of the epidemic in China, 13 but did not analyze the situation of the epidemic abroad. In May 2021, Rokaya Rehouma, Michael Buchert, et al used machine learning for image segmentation and classification to identify patients with COVID‐19 and many ML modules have achieved remarkable predictive results using data sets with limited sample sizes 14–16 …”

Section: Related Workmentioning

confidence: 99%

“…In May 2021, Rokaya Rehouma, Michael Buchert, et al used machine learning for image segmentation and classification to identify patients with COVID‐19 and many ML modules have achieved remarkable predictive results using data sets with limited sample sizes. 14 , 15 , 16 …”

Section: Related Workmentioning

confidence: 99%

An intelligent forecast for COVID‐19 based on single and multiple features

Wang

Zhang

et al. 2022

Int J of Intelligent Sys

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It is urgent to identify the development of the Corona Virus Disease 2019 (COVID-19) in countries around the world. Therefore, visualization is particularly important for monitoring the COVID-19. In this paper, we visually analyze the real-time data of COVID-19, to monitor the trend of COVID-19 in the form of charts. At present, the COVID-19 is still spreading. However, in the existing works, the visualization of COVID-19 data has not established a certain connection between the forecast of the epidemic data and the forecast of the epidemic. To better predict the development trend of the COVID-19, we establish a logistic growth model to predict the development of the epidemic by using the same data source in the visualization. However, the logistic growth model only has a single feature. To predict the epidemic situation in an allround way, we also predict the development trend of the COVID-19 based on the Susceptible Exposed Infected Removed epidemic model with multiple features. We fit the data predicted by the model to the real COVID-19 epidemic data. The simulation results show that the predicted epidemic development trend is consistent with the actual epidemic development trend, and our model performs well in predicting the trend of COVID-19.

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“…[23][24][25][26][27] So far, most researches mainly have focused on the task assignment problem in RCMCS, in which requesters determine what tasks each worker must complete, and workers passively complete the tasks assigned by requesters. Essentially speaking, task assignment problems in MCS are almost the mathematical optimization problems with various goals and constraints, such as sensing quality, [28][29][30][31] incentive cost, 32,33 location privacy, [34][35][36][37][38][39] and social surplus. 40 For example, 32 focused on the problem in task assignment which aimed to minimize the incentive cost under the minimum level of sensing quality constraint.…”

Section: Task Assignment In Mobile Crowdsensingmentioning

confidence: 99%

Task bundling in worker‐centric mobile crowdsensing

Zhao

Yang

Wang

et al. 2021

Int J of Intelligent Sys

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Most existing research about task allocation in mobile crowdsensing mainly focus on requester‐centric mobile crowdsensing (RCMCS), where the requester assigns tasks to workers to maximize his/her benefits. A worker in RCMCS might suffer benefit damage because the tasks assigned to him/her may not maximize his/her benefit. Contrarily, worker‐centric mobile crowdsensing (WCMCS), where workers autonomously select tasks to accomplish to maximize their benefits, does not receive enough attention. The workers in WCMCS can maximize their benefits, but the requester in WCMCS will suffer benefit damage (cannot maximize the number of expected completed tasks). It is hard to maximize the number of expected completed tasks in WCMCS, because some tasks may be selected by no workers, while others may be selected by many workers. In this paper, we apply task bundling to address this issue, and we formulate a novel task bundling problem in WCMCS with the objective of maximizing the number of expected completed tasks. To solve this problem, we design an algorithm named LocTrajBundling which bundles tasks based on the location of tasks and the trajectories of workers. Experimental results show that, compared with other algorithms, our algorithm can achieve a better performance in maximizing the number of expected completed tasks.

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Efficient and Secure Outsourcing of Differentially Private Data Publishing With Multiple Evaluators

Cited by 51 publications

References 40 publications

A long short‐term memory‐based model for greenhouse climate prediction

A long short‐term memory‐based model for greenhouse climate prediction

An intelligent forecast for COVID‐19 based on single and multiple features

Task bundling in worker‐centric mobile crowdsensing

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