Multidimensional k-anonymity for protecting privacy using nearest neighborhood strategy

Data mining is the extracting of information or knowledge of the huge amount of data. Privacy preserving data mining is focused on preventing privacy and achieving data mining goals. To Maintain the privacy of data has become a popular issue because it allows sharing of personal data for analysis. To protect user specific data when releasing micro-data, data holders trying to remove or encrypt personal data, for example names and social security numbers. Released information often contains other data, birth date, sex, and postcode that can be linked to publicly available information to re-identify users and to infer information that was not intended for release. k-anonymity is a significant method for protecting privacy in micro-data release or publishing. k-anonymity protect micro-data table released be indistinguishably related to no fewer than k respondents. Partition in k-anonymity are single dimensional. This paper proposes a new multidimensional model, which provides better k-anonymity. We introduce a multidimensional k-anonymity with nearest neighborhood strategy and experimental results show that it performs better ink-anonymity.