A Survey Paper on an Integrated Approach for Privacy Preserving In High Dimensional Data Using Randomized and SVD Algorithm

Data mining is a technique which is used for extraction of knowledge and information from large amount of data collected by hospitals, government and individuals. The term data mining is also referred as knowledge mining from databases. The major challenge in data mining is ensuring security and privacy of data in databases, because data sharing is common at organizational level. The data in databases comes from a number of sources like � medical, financial, library, marketing, shopping record etc so it is foremost task for anyone to keep secure that data. The objective is to achieve fully privacy preserved data without affecting the data utility in databases. i.e. how data is used or transferred between organizations so that data integrity remains in database but sensitive and confidential data is preserved. This paper presents a brief study about different PPDM techniques like- Randomization, perturbation, Slicing, summarization etc. by use of which the data privacy can be preserved. The technique for which the best computational and theoretical outcome is achieved is chosen for privacy preserving in high dimensional data.