| Type of Material: | Thesis |
| Title: | Anonymization based privacy preservation over cloud data using incremental clustering and map reduce approach on big data |
| Researcher: | NIKKATH BUSHRA S |
| Guide: | Chandrasekar A |
| Department: | Department of Engineering and Technology(Computer Science and Engineering) |
| Publisher: | Bharath University, Chennai |
| Place: | Chennai |
| Year: | 2017 |
| Language: | English |
| Subject: | Elastic Cloud Computing | Cloud Services | Computer Science and Applications | Engineering and Technology |
| Dissertation/Thesis Note: | PhD |
| Fulltext: | Shodhganga |
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| 001 | 454413 | |
| 003 | IN-AhILN | |
| 005 | 2024-09-11 12:27:32 | |
| 008 | __ | 240911t2017||||ii#||||g|m||||||||||eng|| |
| 035 | __ | |a(IN-AhILN)th_454413 |
| 040 | __ | |aBHAU_600073|dIN-AhILN |
| 041 | __ | |aeng |
| 100 | __ | |aNIKKATH BUSHRA S|eResearcher |
| 110 | __ | |aDepartment of Engineering and Technology(Computer Science and Engineering)|bBharath University, Chennai|dChennai|ein |
| 245 | __ | |aAnonymization based privacy preservation over cloud data using incremental clustering and map reduce approach on big data |
| 260 | __ | |aChennai|bBharath University, Chennai|c2017 |
| 300 | __ | |dDVD |
| 502 | __ | |bPhD |
| 518 | __ | |oDate of Registration|d2009-05-13 |
| 520 | __ | |anewlinequotThe major challenge of privacy preservation over the cloud is handling the incremental data because the cloud data may be updated continuously. In this thesis, an incremental clustering technique called K-Means incremental clustering is used over incremental cloud data to efficiently cluster and update huge-volume of incremental data sets. Given a set of records as input choose some privacy sensitive attributes as quasi-identifiers for anonymization. After anonymization, the records are clustered based on K-Means incremental clustering technique, check the k-anonymity constraint, information loss for everynewlinecluster and modify the cluster based on the K-Anonymity constraint. The proposed technique is compared with the existing Xuyun Zhang et al. s technique based on the updating time by adding different number of records. The time taken to update is compared for different k values. Proposed system performs well when numbers of records are large. Evaluation results have demonstrated that the e |
| 650 | __ | |aComputer Science and Applications|2UGC |
| 650 | __ | |aEngineering and Technology|2AIU |
| 653 | __ | |aElastic Cloud Computing |
| 653 | __ | |aCloud Services |
| 700 | __ | |aChandrasekar A|eGuide |
| 856 | __ | |uhttp://shodhganga.inflibnet.ac.in/handle/10603/156350|yShodhganga |
| 905 | __ | |afromsg |
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