Privacy preservation of data using SG and SS models
Ishwarya MV, Saptha Maaleekaa S, Swetha G and Anu Grahaa R
The paper aims at preserving the security of the military database when it is mined to judge about the soldier’s performance without bias or to obtain statistical information. In the existing methods of privacy preservation, the database is sliced, bucketized and the tuples inside the bucket are shuffled. In addition to these processes some of the values are suppressed in order to prevent the sensitive information from being known to the miners. This masking technique decreases the efficiency of classification. These techniques also consume a lot of time. To overcome these problems, we introduce a technique in which generalization is done only to certain tuples of an attribute and then the table is sliced. In one of the sliced tables selective tuples are shuffled based on an algorithm. By selective generalization, classification can be done efficiently and by selective shuffling, less time is consumed. Thus the proposed technique ensures that the miner can mine efficiently with the table provided and at the same time privacy is preserved.
How to cite this article:
Ishwarya MV, Saptha Maaleekaa S, Swetha G, Anu Grahaa R. Privacy preservation of data using SG and SS models. Pharma Innovation 2017;6(10):412-415.