Data Verification of Logical Pk-Anonymization with Big Data Application and Key Generation in Cloud Computing
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. Purp...
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Journal of Function Spaces |
| Online Access: | http://dx.doi.org/10.1155/2022/8345536 |
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| _version_ | 1832549199260942336 |
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| author | Sindhe Phani Kumar R. Anandan |
| author_facet | Sindhe Phani Kumar R. Anandan |
| author_sort | Sindhe Phani Kumar |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-d74f998e45d34bcaba8ee7acce38e45c |
| institution | Kabale University |
| issn | 2314-8888 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Function Spaces |
| spelling | doaj-art-d74f998e45d34bcaba8ee7acce38e45c2025-02-03T06:11:55ZengWileyJournal of Function Spaces2314-88882022-01-01202210.1155/2022/8345536Data Verification of Logical Pk-Anonymization with Big Data Application and Key Generation in Cloud ComputingSindhe Phani Kumar0R. Anandan1Department of CSEDepartment of CSEBackground. 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.http://dx.doi.org/10.1155/2022/8345536 |
| spellingShingle | Sindhe Phani Kumar R. Anandan Data Verification of Logical Pk-Anonymization with Big Data Application and Key Generation in Cloud Computing Journal of Function Spaces |
| title | Data Verification of Logical Pk-Anonymization with Big Data Application and Key Generation in Cloud Computing |
| title_full | Data Verification of Logical Pk-Anonymization with Big Data Application and Key Generation in Cloud Computing |
| title_fullStr | Data Verification of Logical Pk-Anonymization with Big Data Application and Key Generation in Cloud Computing |
| title_full_unstemmed | Data Verification of Logical Pk-Anonymization with Big Data Application and Key Generation in Cloud Computing |
| title_short | Data Verification of Logical Pk-Anonymization with Big Data Application and Key Generation in Cloud Computing |
| title_sort | data verification of logical pk anonymization with big data application and key generation in cloud computing |
| url | http://dx.doi.org/10.1155/2022/8345536 |
| work_keys_str_mv | AT sindhephanikumar dataverificationoflogicalpkanonymizationwithbigdataapplicationandkeygenerationincloudcomputing AT ranandan dataverificationoflogicalpkanonymizationwithbigdataapplicationandkeygenerationincloudcomputing |