Toward better data veracity in mobile cloud computing: A context-aware and incentive-based reputation mechanism

Lin, Hui; Hu, Jia; Tian, Youliang; Yang, Li; Xu, Li

doi:10.1016/j.ins.2016.12.031

Cited by 17 publications

(17 citation statements)

References 34 publications

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“…Category-based context-aware and recommendation incentive reputation mechanism (Lin et al, 2017;? );…”

Section: Mobile Cloud Computationmentioning

confidence: 99%

“…Thus, it is important to maintain the veracity of the data. In (Lin et al, 2017;Lin et al, 2015), the authors proposed a new category-based context-aware and recommendation incentive reputation mechanism (CCRM) that addresses data worthiness to improve the data veracity, with a reputation mechanism that is used as a defense against any internal attacks in MCC. In this mechanism, the data are classified based on the required security level, a higher security level leads to a stricter reputation mechanism.…”

Section: Internet Of Things Applicationsmentioning

confidence: 99%

“…Lin et al (Lin et al, 2017;Lin et al, 2015) 100 mobile clients, 10 routers, 10 services providers Data collected via Mobile cloud computing Improve the data veracity through category-based context-awareness and a recommendation incentive reputation mechanism to defend any internal attacks.…”

Section: Sensors Datamentioning

confidence: 99%

“…In addition, the crowdsourcing technique depends on the contributions of different parties to evaluate data sources. Such an approach is applicable for social media applications (Agarwal et al, 2016), as well as for web applications (McArdle and Kitchin, 2016) and IoT applications (Lin et al, 2017;Lin et al, 2015).…”

Section: Challenges and Future Directionsmentioning

confidence: 99%

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Methods for Assessing, Predicting, and Improving Data Veracity: A survey

Assiri

2020

ADCAIJ

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Data is an essential part of smart cities, and data can play an important role indecision making processes. Data generated through web applications and devicesutilize the Internet of Things (IoT) and related technologies. Thus, it is also importantto be able to create big data, which has historically been defined as having threekey dimensions: volume, variety, and velocity. However, recently, veracity has beenadded as the fourth dimension. Data veracity relates to the quality of the data. Anypotential issues with the quality of the data must be corrected because low-quality dataleads to poor software construction, and ultimately bad decision making. In this work,we reviewed the existing literature on related technical solutions that address dataveracity based on the domain of its application, including social media, web, and IoTapplications. The challenges or limitations and related gaps in existing work will bediscussed, and future research directions will be proposed to address the critical issuesof data veracity in the era of big data

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“…Category-based context-aware and recommendation incentive reputation mechanism (Lin et al, 2017;? );…”

Section: Mobile Cloud Computationmentioning

confidence: 99%

Section: Internet Of Things Applicationsmentioning

confidence: 99%

Section: Sensors Datamentioning

confidence: 99%

Section: Challenges and Future Directionsmentioning

confidence: 99%

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Methods for Assessing, Predicting, and Improving Data Veracity: A survey

Assiri

2020

ADCAIJ

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“…The prominence of the RDF data shows [8] and the RDF schema language (RDFS) [9] is expected to the adaptable and extensible portrayal of data under the type of triples (subjects, objects, and predicates). Creating metadata for the web is the fundamental objective of the RDF plan [10].…”

Section: Introductionmentioning

confidence: 99%

Cloud Based RDF Security: A Secured Data Model for Cloud Computing

Arulanandu¹,

Murthy²,

Nagraj³

2018

IJIES

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Abstract:In this paper, a new secured data model for cloud computing is proposed which uses partial resource description framework (RDF) encryption and token based access control system in which sensitive data in an RDF graph is encrypted and all other non-sensitive data are publicly lucid. The security process, the decryption process, and query process are the three essential procedures in this framework. The consequence of the security process is the encrypted data, encrypted metadata, and plain text fragments. The proposed technique permits the token based access control system for the decryption procedure. The query process incorporates the map reduce framework is for lessening the immense measure of employments. At long last, the query answer is sent to the user in light of the access token list (AT-list) of the system administrator. Our test comes about demonstrate that, the performance of the proposed technique is assessed in view of the precision, recall and execution time of the framework. Our proposed approach is actualized using Java and keep running on Windows XP framework and the Lehigh University Benchmark (LUBM) datasets are used for our examination. In the paper this new secured RDF data model is deployed and tested using AWS elastic beanstalk.

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A k‐nearest neighbor query method based on trust and location privacy protection

Guo

Zhu

Yang

et al. 2020

Concurrency and Computation

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Spatial query is an important supporting technology in the Internet of Things (IoT) and location-based services (LBS). The k-nearest neighbor query is widely used for spatial queries. However, user location privacy may be leaked in the query. In addition, some users are malicious or uncooperative. With the objective of overcoming these problems, a k-nearest neighbor query method based on trust and location privacy protection is proposed. First, we employ a new K-anonymity method based on cooperation to protect a query user's location privacy. In this method, the query user constructs an anonymous group by introducing a trust mechanism to incentivize cooperation among users. Then, according to the different radii of the selection area set by the query user, agent users with higher reputation values who send query requests for the query user are selected. Finally, the agent users obtain the query results from the LBS server and forward them to the query user, and the query user screens the results according to his or her real location. The experiments show that our method can effectively stimulate users to cooperate, better exclude malicious users, and improve the accuracy of the query results while protecting the privacy of the query user.

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Toward better data veracity in mobile cloud computing: A context-aware and incentive-based reputation mechanism

Cited by 17 publications

References 34 publications

Methods for Assessing, Predicting, and Improving Data Veracity: A survey

Methods for Assessing, Predicting, and Improving Data Veracity: A survey

Cloud Based RDF Security: A Secured Data Model for Cloud Computing

A k‐nearest neighbor query method based on trust and location privacy protection

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