A General Fine-Grained Truth Discovery Approach for Crowdsourced Data Aggregation

2017 ◽  
pp. 3-18 ◽  
Author(s):  
Yang Du ◽  
Hongli Xu ◽  
Yu-E Sun ◽  
Liusheng Huang
Keyword(s):  
Data Aggregation ◽  
Fine Grained ◽  
Truth Discovery ◽  
Crowdsourced Data

Lightweight and Fine-Grained Privacy-Preserving Data Aggregation Scheme in Edge Computing

2021 ◽  
pp. 1-10
Author(s):  
Hongyang Li ◽  
Qingfeng Cheng ◽  
Xinghua Li ◽  
Siqi Ma ◽  
Jianfeng Ma
Keyword(s):  
Data Aggregation ◽  
Privacy Preserving ◽  
Edge Computing ◽  
Fine Grained ◽  
Aggregation Scheme

scholarly journals A Secure Truth Discovery for Data Aggregation in Mobile Crowd Sensing

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Taochun Wang ◽  
Chengmei Lv ◽  
Chengtian Wang ◽  
Fulong Chen ◽  
Yonglong Luo
Keyword(s):  
Data Aggregation ◽  
Rapid Development ◽  
Mobile Crowd Sensing ◽  
Sense Data ◽  
Sensing Systems ◽  
Crowd Sensing ◽  
Truth Discovery ◽  
Head Node ◽  
True Values ◽  
Mobile Crowd

With the rapid development of portable mobile devices, mobile crowd sensing systems (MCS) have been widely studied. However, the sensing data provided by participants in MCS applications is always unreliable, which affects the service quality of the system, and the truth discovery technology can effectively obtain true values from the data provided by multiple users. At the same time, privacy leaks also restrict users’ enthusiasm for participating in the MCS. Based on this, our paper proposes a secure truth discovery for data aggregation in crowd sensing systems, STDDA, which iteratively calculates user weights and true values to obtain real object data. In order to protect the privacy of data, STDDA divides users into several clusters, and users in the clusters ensure the privacy of data by adding secret random numbers to the perceived data. At the same time, the cluster head node uses the secure sum protocol to obtain the aggregation result of the sense data and uploads it to the server so that the server cannot obtain the sense data and weight of individual users, further ensuring the privacy of the user’s sense data and weight. In addition, using the truth discovery method, STDDA provides corresponding processing mechanisms for users’ dynamic joining and exiting, which enhances the robustness of the system. Experimental results show that STDDA has the characteristics of high accuracy, low communication, and high security.

Truth discovery for spatio-temporal events from crowdsourced data

2017 ◽  
Vol 10 (11) ◽  
pp. 1562-1573 ◽  
Author(s):  
Daniel A. Garcia-Ulloa ◽  
Li Xiong ◽  
Vaidy Sunderam
Keyword(s):  
Truth Discovery ◽  
Crowdsourced Data ◽  
Spatio Temporal ◽  
Temporal Events

scholarly journals PLDP: Personalized Local Differential Privacy for Multidimensional Data Aggregation

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Zixuan Shen ◽  
Zhihua Xia ◽  
Peipeng Yu
Keyword(s):  
Data Aggregation ◽  
Differential Privacy ◽  
Frequency Estimation ◽  
Multidimensional Data ◽  
Public Concern ◽  
Personal Privacy ◽  
Crowdsourced Data ◽  
Protection And Utilization ◽  
High Utility ◽  
Privacy Issues

The collection of multidimensional crowdsourced data has caused a public concern because of the privacy issues. To address it, local differential privacy (LDP) is proposed to protect the crowdsourced data without much loss of usage, which is popularly used in practice. However, the existing LDP protocols ignore users’ personal privacy requirements in spite of offering good utility for multidimensional crowdsourced data. In this paper, we consider the personality of data owners in protection and utilization of their multidimensional data by introducing the notion of personalized LDP (PLDP). Specifically, we design personalized multiple optimized unary encoding (PMOUE) to perturb data owners’ data, which satisfies ϵ total -PLDP. Then, the aggregation algorithm for frequency estimation on multidimensional data under PLDP is developed, which is described in two situations. Experiments are conducted on four real datasets, and the results show that the proposed aggregation algorithm yields high utility. Moreover, case studies with four real datasets demonstrate the efficiency and superiority of the proposed scheme.

scholarly journals SmartX Multi-View Visibility Framework with Flow-Centric Visibility for SDN-Enabled Multisite Cloud Playground

2019 ◽  
Vol 9 (10) ◽  
pp. 2045 ◽  
Author(s):  
Muhammad Usman ◽  
Muhammad Ahmad Rathore ◽  
JongWon Kim
Keyword(s):  
Data Aggregation ◽  
Communication Technologies ◽  
Information Communication Technologies ◽  
Information Communication ◽  
Cloud Infrastructure ◽  
Application Performance ◽  
Fine Grained ◽  
Effective Operation ◽  
Main Components ◽  
Virtual Resources

Modern information communication technologies (ICT) infrastructures are getting complicated to cope with the various demands needed to accommodate the emerging technology paradigms such as cloud, software-defined networking (SDN), and internet of things (IoT). Visibility is essential for the effective operation of such modern ICT infrastructures to easily pinpoint server faults, network bottlenecks, and application performance troubles. Even though many conventional monitoring solutions are now available, multi-layer visibility is still limited when operating such complicated infrastructures. To address this particular limitation, a futuristic multi-layer visibility framework denoted as SmartX multi-view visibility framework (MVF), is proposed for unifying the visibility of underlay, physical and virtual resources, flow, and workload layers. To unify multi-layer visibility, this paper presents a comprehensive extension of SmartX MVF with flow-centric visibility for simultaneously monitoring physical-virtual resources, flows classification, and visualization to eventually assist secured operation of SDN-enabled multisite cloud infrastructure. Flow-centric visibility design has five main components (1) a lightweight network packets-precise flows visibility collection component, (2) a visibility data aggregation and tagging component, (3) a multi-layer visibility data integration component, (4) a non-learning-based network packets flows classification component, and (5) a visualization component. Furthermore, a prototype implementation of SmartX MVF with flow-centric visibility is deployed in an SDN-enabled multisite cloud playground to verify the proposed multi-view visibility of fine-grained flow-aware physical-virtual resources.

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