A Novel Technique for Privacy Preserving Data Publishing

Data Anonymization is technique used for organizing of data. Privacy Preserving Data Mining that is the study of data mining lot of Problems on privacy, it given growing thoughtfulness from the investigation community. Privacy-preservation data publishing has received lot of thoughtfulness, as it is always a problem of how to protect database of high dimension. In much organization where large number of personal data is available, such data must be protected. The personal data may be misused, for a variety of resolutions. In order to improve these apprehensions, a number of techniques have recently been proposed in order to perform the data mining tasks in a privacy-preserving way. There are several anonymization techniques available such as generalization and bucketization that are designed for privacy Preservation of micro data publishing. But it has been seen that for high dimension data generalization misses the information, bucketization on other hand does not prevent membership discovery. Here we are implementing additional anonymization technique known as Slicing. The consequence of using slicing is that it can switch high dimension data. Slicing conserves better data service than generalization and also prevents participation revelation. Here we focus on operational method that can be used for providing better data effectiveness and can handle high-dimensional data.