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 conscientiously reviewed and chosen from lots of submissions. The papers are equipped in topical sections on database and knowledge structures; disbursed software program improvement; human desktop interplay and interface; ICT; web and net computing; cellular computing; multi agent structures; multimedia and video platforms; parallel and allotted algorithms; protection, belief and privateness.
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Extra info for Advances in Computing and Communications: First International Conference, ACC 2011, Kochi, India, July 22-24, 2011. Proceedings, Part II
Tanusha S. Nadkarni, Radhesh Mohandas, and Alwyn R. Pais XXXV 685 SQL Injection Disclosure Using BLAH Algorithm . . . . . . . . . . K. Sherly 693 Author Index . . . . . . . . . . . . . . . . . . . . . . . . . in Abstract. Organizations need to anonymize the data before releasing them so that data mining cannot predict private information. It’s the duty of every organization to ensure privacy of its stakeholders. There is a tradeoff between privacy and utility of the released data.
The development of a DW system is also different from the development of a conventional operational system. Operational system has to fulfill the application specific business needs, whereas, the design of DW not only involves the information requirements of the user but the structure of the underlying source system. So, the development of a DW is complex and requires an appropriate lifecycle to be followed . In  a comparison of the data/ supply-driven, requirements/user-driven and goal-driven methodologies for DW development using a case study is explained.
Fig. 6 shows the variation of utility and privacy with k. It clearly follows from the figure that on increasing the value of k privacy provided by the dataset increases but utility decreases. For this sample dataset the balancing point comes between k=8 and k=9, and utility of the dataset at balancing point is around 60%. Fig. 5. Classification Result for 3-Anonymized Dataset Fig. 6. 2 Experiment 2 In the second experiment all attributes were considered for analysis, to study the effect of more number of attributes on the privacy and the utility of the k-anonymized dataset.