Preservation of Privacy using Multidimensional K-Anonymity Method for Non-Relational Data

Mining of huge data having complexity is a challenging issue also maintaining Privacy of data is also equally important ,sometimes there is a need to release data for use of researchers or for the purpose of gaining knowledge or earn money this release of data includes releas e of all attributes of personal data. when this type of data like Insurance record data, Medical diagnosis data, funding scheme data is release even if we remove sensitive attribute like Name for hiding personal details still data re-identification is possible by linking public data like voters data with these released data and by linking the quasi identifiers we are able to get sensitive information about person like critical disease, financial position etc. by applying k–Anonymization using multiple dimensions of attributes we are able to hide these sensitive attributes by generalising and suppressing the Quasi identifiers so that when linking with public database is done no records are re-identified, also we obtained results for quality measures for anonymisation and observed that the value of k once we start increase after some threshold anonymity starts decreasing so there is a need to choose proper value of k on non-relational data