Decomposition: Privacy Preservation for Multiple Sensitive Attributes
نویسندگان
چکیده
Aiming at ensuring privacy preservation in personal data publishing, the topic of anonymization has been intensively studied in recent years. However, existing anonymization techniques all assume each tuple in the microdata table contains one single sensitive attribute (the SSA case), while none paid attention to the case of multiple sensitive attributes in a tuple (the MSA case). In this paper, we conduct the pioneering study on the MSA case, observe new privacy risks, and reason why generalization, the most common approach for anonymization, is impractical in this case. Instead, we propose a new framework, decomposition, to tackle privacy preservation in the MSA case. We elaborate decompose by extending it naturally from the SSA case and introducing the (l1, l2, . . . , ld)-diversity principle. Experiments with real data verify the effectiveness of decomposition.
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