Abstract
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, and propose a new framework, decomposition, to tackle privacy preservation in the MSA case.
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Ye, Y., Liu, Y., Wang, C., Lv, D., Feng, J. (2009). Decomposition: Privacy Preservation for Multiple Sensitive Attributes. In: Zhou, X., Yokota, H., Deng, K., Liu, Q. (eds) Database Systems for Advanced Applications. DASFAA 2009. Lecture Notes in Computer Science, vol 5463. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00887-0_42
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DOI: https://doi.org/10.1007/978-3-642-00887-0_42
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-00886-3
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