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Attacks on Anonymization-Based Privacy-Preserving: A Survey for Data Mining and Data Publishing

Data mining is the extraction of vast interesting patterns or
knowledge from huge amount of data. The initial idea of privacy-preserving
data mining PPDM was to extend traditional data mining techniques to work with
the data modified to mask sensitive information. The key issues were how to
modify the data and how to recover the data mining result from the modified
data. Privacy-preserving data mining considers the problem of running data mining
algorithms on confidential data that is not supposed to be revealed even to the
party running the algorithm. In contrast, privacy-preserving
data publishing (PPDP) may not necessarily be tied to a specific data mining
task, and the data mining task may be unknown at the time of data publishing.
PPDP studies how to transform raw data into a version that is immunized against
privacy attacks but that still supports effective data mining tasks. Privacy-preserving
for both data mining (PPDM) and data publishing (PPDP) has become increasingly
popular because it allows sharing of privacy sensitive data for analysis
purposes. One well studied approach is the k-anonymity model [1]
which in turn led to other models such as confidence bounding, l-diversity,
t-closeness, (α,k)-anonymity, etc. In particular, all known mechanisms try to
minimize information loss and such an attempt provides a loophole for attacks.
The aim of this paper is to present a survey for most of the common attacks
techniques for anonymization-based PPDM & PPDP and explain their effects
on Data Privacy.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

*Journal of Information Security*, Vol. 4 No. 2, 2013, pp. 101-112. doi: 10.4236/jis.2013.42012.

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