Fuzzy Based Approach for Privacy Preserving Publication of Data
Source: Andhra University
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.