A quasi-identifier refers to a subset of attributes that can uniquely identify most tuples in a table. Incautious publication of quasi-identifiers will lead to privacy leakage. In this paper we consider the problems of finding and masking quasi-identifiers. Both problems are provably hard with severe time and space requirements. We focus on designing efficient approximation algorithms for large data sets. We first propose two natural measures for quantifying quasi-identifiers: distinct ratio and separation ratio. We develop efficient algorithms that find small quasi-identifiers with provable size and separation/distinct ratio guarantees, with space and time requirements sublinear in the number of tuples. We also propose efficient algorithms for masking quasi-identifiers, where we use a random sampling technique to greatly reduce the space and time requirements, without much sacrifice in the quality of the results. Our algorithms for masking and finding quasi-identifiers naturally apply to stream databases. Extensive experimental results on real world data sets confirm efficiency and accuracy of our algorithms.
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