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  An Efficient Sliced Data Algorithm Design for Data Protection  
  Authors : G. Hima Bindhu; G. Hima Bindhu
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Today, most enterprises are actively collecting and storing data in large databases. Privacy has become a key issue for progress in data mining. Maintaining the privacy of data mining has become increasingly popular because it allows sharing of privacy-sensitive data for analysis. Privacypreserving data mining is used to safeguard sensitive information from unsanctioned disclosure. Privacy is an important issue in data publishing years because of the increasing ability to store personal data about users. Privacypreserving data publishing (PPDP) provides methods and tools for publishing useful information while preserving data privacy. A number of techniques such as bucketization, generalization have been proposed to perform privacypreserving data mining. Recent work has shown that generalization not support for high- dimensional data. Bucketization cannot prevent membership disclosure and does not apply for data that do not have a clear separation between quasi-identifying attributes and sensitive attributes. A new technique is introduced that is known as slicing, which partitions the data both horizontally and vertically. Slicing provides better data utility than generalization and can be used for membership disclosure protection. Slicing can handle high dimensional data. Also slicing can be used for attribute disclosure protection and develop an efficient algorithm for computing the sliced data that obey the l-diversity requirement. Slicing is more effective than bucketization in workloads involving the sensitive attribute. Another advantage of slicing can be used to prevent membership disclosure.


Published In : IJCSN Journal Volume 3, Issue 4

Date of Publication : 01 August 2014

Pages : 187 - 190

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Publication Link : An Efficient Sliced Data Algorithm Design for Data Protection




G. Hima Bindhu : M.Tech, CSE, LBRCE, Mylavaram, India

Dr. S. Sai Satyanarayana Reddy : Professor, CSE, LBRCE, Mylavaram, India








Data publishing




The slicing strategy overcomes the limitations of generalization and bucketization methods. It preserves better utility while protecting against privacy threats where each attribute is exactly in one column. An extension of slicing is overlapping slicing which duplicates an attribute in more than one column. The proposed tuple grouping algorithm is optimized ldiversity check algorithm which obtains more effective tuple grouping and provides the secure data. Another advantage of slicing is that it can handle high dimensional data.Its future work can be as privacy preservation as the big issue, large number of datasets is increasing security to such data must be available. Therefore, as the term privacy entered encryption and decryption and compression can further be done for such databases.










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