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Incremental $k$ -Anonymous Microaggregation in Large-Scale Electronic Surveys With Optimized Scheduling
2018
IEEE Access
Improvements in technology have led to enormous volumes of detailed personal information made available for any number of statistical studies. This has stimulated the need for anonymization techniques striving to attain a difficult compromise between the usefulness of the data and the protection of our privacy. k-Anonymous microaggregation permits releasing a dataset where each person remains indistinguishable from other k−1 individuals, through the aggregation of demographic attributes,
doi:10.1109/access.2018.2875949
fatcat:gab7xhvd4jbbvef4oaekiligoq