Fuzzy Based Approach for Privacy Preserving Publication of Data

Data privacy is the most acclaimed problem when publishing individual data. It ensures individual data publishing without disclosing sensitive data. The much popular approach, is K-Anonymity, where data is transformed to equivalence classes, each class having a set of K- records that are indistinguishable from each other. But several authors have pointed out numerous problems with K-anonymity and have proposed techniques to counter them or avoid them. L-diversity and t-closeness are such techniques to name a few. The paper has shown that all these techniques increase computational effort to practically infeasible levels, though they increase privacy. A few techniques account for too much of information loss, while achieving privacy.