A Novel Approach of Data Sanitization using Privacy Preserving Data Mining

Privacy preserving data mining (PPDM) is a popular as well as interesting topic in the research community. The important issue is how to make a balance between privacy protection and knowledge discovery in the sharing process. One of the existing privacy preserving utility mining and two algorithms, HHUIF (Hiding High utility item First Algorithm) and MSICF (Maximum Sensitive ItemsetsConict First algorithm), to conceal the sensitive itemsets so that the antagonist cannot mine them from the modified database. The work also minimizes the impact on the sanitized database of hiding sensitive item sets. In order to address this sanitization we introduced a privacy preserving data mining using secure hash algorithm technique to modify itemset based on threshold value. We primarily focus on protecting privacy in database. By finding sensitive itemset we calculate SHA of these sensitive itemset and apply proposed algorithm to modify itemset. On different value of threshold we calculate value of hiding failure and miss cost. At last we summarized that as value of threshold increased value of hiding failure and missing cost decreased.