An Approach to Detect Malicious Feedback Rating for Measuring Web Service Reputation

Kumar, Sushant

doi:10.14257/ijgdc.2016.9.3.13

Cited by 3 publications

(2 citation statements)

References 12 publications

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“…Kumar and Nayak [15] verified a tool-based really sure look for awesome anonymization. When the rate of okay is low, this method works properly.…”

Section: Research Surveymentioning

confidence: 98%

“…Furthermore, the simultaneous arrival of the calculations permits for recouping from midway decline of allocated server or storage performance in some unspecified time in the destiny throughout the method [14]. When one mapper or reducer fails, the task is rescheduled, permitting the facts to be beneficial [15]. Figure 2 shows the map's and reducer's art work flow.…”

Section: Introductionmentioning

confidence: 99%

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Data Verification of Logical Pk-Anonymization with Big Data Application and Key Generation in Cloud Computing

Kumar

Anandan

2022

Journal of Function Spaces

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Background. As more data becomes available about how frequently the cloud can be updated, a more comprehensive picture of its safety is emerging. The suggested artworks use a cloud-based gradual clustering device to cluster and refresh a large number of informational indexes in a useful manner. Purpose. Anonymization of data is done at the point of collection in order to safeguard the data. More secure than K-Anonymization, Pk-Anonymization is the area’s first randomization method. A cloud service provider (CSP) is an independent company that provides a cloud-based network and computing resources. Customers’ security and connection protection must be verified by an authority before facts may be transferred to cloud servers for storing information. Method. Logical Pk-Anonymization and key era techniques are proposed in this proposed artwork in order to verify the cloud records, as well as to store sensitive information in the cloud. Cloud-based informational indexes are used in the proposed framework, which is effective at handling large amounts of data through MapReduce; a parallel data preparation form is obtained; to get all information as new facts that joins after a while, information anonymization techniques to carry out each protection and immoderate information utilization while updating take place; information loss and clean time is reduced for substantial amounts of data. As a result, the safety and records software might be in sync.

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“…Kumar and Nayak [15] verified a tool-based really sure look for awesome anonymization. When the rate of okay is low, this method works properly.…”

Section: Research Surveymentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Data Verification of Logical Pk-Anonymization with Big Data Application and Key Generation in Cloud Computing

Kumar

Anandan

2022

Journal of Function Spaces

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Secured data storage in the cloud using logical Pk-Anonymization with Map Reduce methods and key generation in cloud computing

Kumar

Anandan

Tchier

et al. 2021

Journal of Taibah University for Science

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Enhancing Cloud-Based IoT Security Through Trustworthy Cloud Service: An Integration of Security and Reputation Approach

Wang

Lan

et al. 2019

IEEE Access

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An Approach to Detect Malicious Feedback Rating for Measuring Web Service Reputation

Abstract: In

Cited by 3 publications

References 12 publications

Data Verification of Logical Pk-Anonymization with Big Data Application and Key Generation in Cloud Computing

Data Verification of Logical Pk-Anonymization with Big Data Application and Key Generation in Cloud Computing

Secured data storage in the cloud using logical Pk-Anonymization with Map Reduce methods and key generation in cloud computing

Enhancing Cloud-Based IoT Security Through Trustworthy Cloud Service: An Integration of Security and Reputation Approach

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