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. Howev er, 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.

So far, several techniques have been proposed for privacy preserving clustering (PPC). Most of the existing techniques have been designed based on heuristic notions without provable privacy guarantees. ϵ -differential privacy is a strong notion of pr