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In this paper, the authors discussed inference attacks against the anonymization of incremental data. In particular, they discussed three basic types of cross-version inference attacks and presented algorithms for detecting each attack. They also presented some heuristics to address the efficiency of the algorithms. Based on these ideas, they developed secure anonymization algorithms for incremental datasets using two existing anonymization algorithms. They also empirically evaluated the approach by comparing to other approaches. The experimental result showed that the approach outperformed other approaches in terms of privacy and data quality.
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