Advances in Computing and Communications: First by Korra Sathya Babu, Sanjay Kumar Jena (auth.), Ajith Abraham,
By Korra Sathya Babu, Sanjay Kumar Jena (auth.), Ajith Abraham, Jaime Lloret Mauri, John F. Buford, Junichi Suzuki, Sabu M. Thampi (eds.)
This quantity is the second one a part of a four-volume set (CCIS one hundred ninety, CCIS 191, CCIS 192, CCIS 193), which constitutes the refereed court cases of the 1st overseas convention on Computing and Communications, ACC 2011, held in Kochi, India, in July 2011.
The seventy two revised complete papers offered during this quantity have been rigorously reviewed and chosen from lots of submissions. The papers are equipped in topical sections on database and knowledge platforms; disbursed software program improvement; human desktop interplay and interface; ICT; web and net computing; cellular computing; multi agent structures; multimedia and video structures; parallel and allotted algorithms; safeguard, belief and privateness.
Read or Download Advances in Computing and Communications: First International Conference, ACC 2011, Kochi, India, July 22-24, 2011. Proceedings, Part II PDF
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Additional resources for Advances in Computing and Communications: First International Conference, ACC 2011, Kochi, India, July 22-24, 2011. Proceedings, Part II
528–238 (2009) 13. : Including security rules in a MDA approach for secure DWs. In: ARES, pp. 528–238 (2009) 14. : An Improved Security Framework for Data Warehouse: A Hybrid Approach, pp. 1586–1590. IEEE, Los Alamitos (2010) 15. : A Methodological framework for Data Warehouse design. In: Proceedings of the First International Workshop on Data Warehousing and OLAP (1998) 16. : Security in Data Warehouses 17. : Extended RBAC-Based design and Implementation for a Secure Data Warehouse (2007) 18. : A Prototype Model for Data Warehouse Security based on Metadata.
Jena sensitive attributes. Clusters were formed using WEKA according to the value of k. 1, the clusters produced may contain less than k tuples, thus an adjustment was done so that each cluster contains at least k tuples. After adjusting the clusters, k-anonymization is performed based on the generalization hierarchy. The k-anonymization algorithm based on OKA was run to generalize the adjusted clusters. For evaluating utility, we performed the classification mining on the k-anonymized dataset.
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