scholarly journals An Incentive and Reputation Mechanism Based on Blockchain for Crowd Sensing Network

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Zainib Noshad ◽  
Asad Ullah Khan ◽  
Shahid Abbas ◽  
Zain Abubaker ◽  
Nadeem Javaid ◽  
...  
Keyword(s):  
Service Providers ◽  
Reputation System ◽  
Reputation Mechanism ◽  
Privacy Leakage ◽  
Crowd Sensing ◽  
True Value ◽  
Participation Level ◽  
Gas Consumption ◽  
Crowd Sensing Networks ◽  
Fake Reviews

Nowadays, sensors inserted in mobile applications are used for gathering data for an explicit assignment that can effectively save cost and time in crowd sensing networks (CSNs). The true value and essence of gathered statistics depend on the participation level from all the members of a CSN, i.e., service providers, data collectors, and service consumers. In comparison with the centralized conventional mechanisms that are susceptible to privacy invasion, attacks, and manipulation, this article proposes a decentralized incentive and reputation mechanism for CSN. The monetary rewards are used to motivate the data collectors and to encourage the participants to take part in the network activities. Whereas the issue of privacy leakage is dealt with using Advanced Encryption Standard (AES128) technique. Additionally, a reputation system is implemented to tackle issues like data integrity, fake reviews, and conflicts among entities. Through registering reviews, the system encourages data utilization by providing correct, consistent, and reliable data. Furthermore, simulations are performed for analyzing the gas consumed by smart contracts. Similarly, the encryption technique is ratified by comparing its execution time with other techniques that are previously used in literature. Lastly, the reputation system is inspected through analyzing the gas consumption and mining time of input string length.

Experience

2021 ◽  
Vol 13 (1) ◽  
pp. 1-16
Author(s):  
Michela Fazzolari ◽  
Francesco Buccafurri ◽  
Gianluca Lax ◽  
Marinella Petrocchi
Keyword(s):  
Relative Frequency ◽  
Service Providers ◽  
Real Data ◽  
Online Reviews ◽  
Purchase Decision ◽  
The Past ◽  
Supervised Classifiers ◽  
Relative Frequency Distribution ◽  
Opinion Spam ◽  
Fake Reviews

Over the past few years, online reviews have become very important, since they can influence the purchase decision of consumers and the reputation of businesses. Therefore, the practice of writing fake reviews can have severe consequences on customers and service providers. Various approaches have been proposed for detecting opinion spam in online reviews, especially based on supervised classifiers. In this contribution, we start from a set of effective features used for classifying opinion spam and we re-engineered them by considering the Cumulative Relative Frequency Distribution of each feature. By an experimental evaluation carried out on real data from Yelp.com, we show that the use of the distributional features is able to improve the performances of classifiers.

scholarly journals A Blockchain-Based Location Privacy Protection Incentive Mechanism in Crowd Sensing Networks

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3894 ◽  
Author(s):  
Bing Jia ◽  
Tao Zhou ◽  
Wuyungerile Li ◽  
Zhenchang Liu ◽  
Jiantao Zhang
Keyword(s):  
Cloud Computing ◽  
Privacy Protection ◽  
Large Scale ◽  
Location Privacy ◽  
Incentive Mechanism ◽  
User Participation ◽  
Crowd Sensing ◽  
The Incentive Mechanism ◽  
Crowd Sensing Networks ◽  
Location Privacy Protection

Crowd sensing is a perception mode that recruits mobile device users to complete tasks such as data collection and cloud computing. For the cloud computing platform, crowd sensing can not only enable users to collaborate to complete large-scale awareness tasks but also provide users for types, social attributes, and other information for the cloud platform. In order to improve the effectiveness of crowd sensing, many incentive mechanisms have been proposed. Common incentives are monetary reward, entertainment & gamification, social relation, and virtual credit. However, there are rare incentives based on privacy protection basically. In this paper, we proposed a mixed incentive mechanism which combined privacy protection and virtual credit called a blockchain-based location privacy protection incentive mechanism in crowd sensing networks. Its network structure can be divided into three parts which are intelligence crowd sensing networks, confusion mechanism, and blockchain. We conducted the experiments in the campus environment and the results shows that the incentive mechanism proposed in this paper has the efficacious effect in stimulating user participation.

scholarly journals Limiting Privacy Breaches in Average-Distance Query

2020 ◽  
Vol 2020 ◽  
pp. 1-24
Author(s):  
Huihua Xia ◽  
Yan Xiong ◽  
Wenchao Huang ◽  
Zhaoyi Meng ◽  
Fuyou Miao
Keyword(s):  
Service Providers ◽  
Average Distance ◽  
Real Life ◽  
Privacy Preserving ◽  
Business Decision ◽  
Privacy Leakage ◽  
Private Data ◽  
New Type ◽  
Different Dimensions ◽  
Privacy Breaches

Querying average distances is useful for real-world applications such as business decision and medical diagnosis, as it can help a decision maker to better understand the users’ data in a database. However, privacy has been an increasing concern. People are now suffering serious privacy leakage from various kinds of sources, especially service providers who provide insufficient protection on user’s private data. In this paper, we discover a new type of attack in an average-distance query (AVGD query) with noisy results. The attack is general that it can be used to reveal private data of different dimensions. We theoretically analyze how different factors affect the accuracy of the attack and propose the privacy-preserving mechanism based on the analysis. We experiment on two real-life datasets to show the feasibility and severity of the attack. The results show that the severity of the attack is mainly influenced by the factors including the noise magnitude, the number of queries, and the number of users in each query. Also, we validate the correctness of our theoretical analysis by comparing with the experimental results and confirm the effectiveness of the privacy-preserving mechanism.

scholarly journals The Truthful Evolution and Incentive for Large-Scale Mobile Crowd Sensing Networks

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 51187-51199 ◽  
Author(s):  
Yingjie Wang ◽  
Yingshu Li ◽  
Zhongyang Chi ◽  
Xiangrong Tong
Keyword(s):  
Large Scale ◽  
Mobile Crowd Sensing ◽  
Crowd Sensing ◽  
Crowd Sensing Networks ◽  
Mobile Crowd

scholarly journals A Cloud-User Protocol Based on Ciphertext Watermarking Technology

2017 ◽  
Vol 2017 ◽  
pp. 1-14 ◽  
Author(s):  
Keyang Liu ◽  
Weiming Zhang ◽  
Xiaojuan Dong
Keyword(s):  
Cloud Computing ◽  
Service Providers ◽  
Hamming Distance ◽  
Cloud Service ◽  
Third Party ◽  
Plain Text ◽  
Privacy Leakage ◽  
Cloud Computing Service ◽  
The Right ◽  
Cloud User

With the growth of cloud computing technology, more and more Cloud Service Providers (CSPs) begin to provide cloud computing service to users and ask for users’ permission of using their data to improve the quality of service (QoS). Since these data are stored in the form of plain text, they bring about users’ worry for the risk of privacy leakage. However, the existing watermark embedding and encryption technology is not suitable for protecting the Right to Be Forgotten. Hence, we propose a new Cloud-User protocol as a solution for plain text outsourcing problem. We only allow users and CSPs to embed the ciphertext watermark, which is generated and embedded by Trusted Third Party (TTP), into the ciphertext data for transferring. Then, the receiver decrypts it and obtains the watermarked data in plain text. In the arbitration stage, feature extraction and the identity of user will be used to identify the data. The fixed Hamming distance code can help raise the system’s capability for watermarks as much as possible. Extracted watermark can locate the unauthorized distributor and protect the right of honest CSP. The results of experiments demonstrate the security and validity of our protocol.

scholarly journals Co-Creation of Public Services; Levels of Customers’ Participation

2018 ◽  
Vol 2 (1) ◽  
pp. 40-47
Author(s):  
Milda Damkuviene ◽  
Evandzelina Petukiene
Keyword(s):  
Service Management ◽  
Public Services ◽  
Service Providers ◽  
Customer Participation ◽  
Critical Research ◽  
Client Participation ◽  
Research Issues ◽  
Participation Level ◽  
Level Data ◽  
Service Theory

Customer participation is one of the critical research issues in service management. This study draws on the client participation concept to explore the content and levels of customer participation in public services (Lithuanian elderships). By integrating Unified Service theory, Service Dominant logic, and using a research design with 12 interviews and 600 participating customer survey, the study confirms the three level customer participation model, identifies four categories of participating customers and shows how sociodemographic characteristics affect customer participation level. Data suggest that public service providers need to pay attention to customer participation management (identification, selection, education and motivation).

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