A ROBUST TECHNIQUE FOR PRIVACY PRESERVATION OF OUTSOURCED TRANSACTION DATABASE

Privacy Preserving Data Mining (PPDM) is used to extract relevant knowledge from large amount of data and at the same time protect the sensitive information from the data miners. The enhancement of data mining research will be the development of techniques that incorporate privacy concerns. This paper provides an enhanced technique for preserving privacy of association rules as well as private data of individuals in an outsourced business transaction database. As the importance of business transaction data has increased manifolds and the data has become an essential part of any business. This paper implement privacy by using a perturbation technique using jointly Gaussian Function that will not only maintain the privacy of association rules present in the dataset but also the sensitive attributes of individuals contained in it. Using this approach we are reducing time complexity, space complexity, and fake and false rules problems.