Affiliations: [a] Department of Computer Engineering, Miyaneh Branch, Islamic Azad University, Miyaneh, Iran | [b] School of Computer Engineering, Iran University of Science and Technology, Tehran, Iran
Correspondence:
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Corresponding author: Mohammad Abdollahi Azgomi, School of Computer Engineering, Iran University of Science and Technology, Hengam St., Resalat Sq., Tehran 16846-13114, Iran. Fax: +98 21 7322 5322; E-mail:azgomi@iust.ac.ir
Abstract: The goal of privacy preserving clustering (PPC) is to
preserve the privacy of data during clustering analysis. Most of the
existing PPC algorithms are based on heuristic notions without provable
privacy. Differential privacy is the strong notion of privacy introduced to
overcome this problem. However, the lower degree of utility is the serious
drawback of the techniques, which preserve differential privacy. In
addition, high dimensionality of data is another drawback of the most
existing PPC techniques, which leads to low efficiency of them. This paper
proposes differential-based algorithms for PPC in horizontally and
vertically distributed datasets. To overcome the above two drawbacks, we
have used orthogonal discrete wavelet transforms (DWT) for obtaining
perturbed data with both low data dimensionality and less noise addition.
Our algorithms are implemented and experimented using some well-known
datasets. The results show that the proposed algorithms guarantee an
appropriate level of both utility and privacy of the published data.
