  Providing public access to unprotected digital data can pose a threat of
unwanted disclosing the restricted information. The problem of protecting such
information can be divided into two main subclasses, namely, individual and
group data anonymity. By group anonymity we define protecting important data
patterns, distributions, and collective features which cannot be determined
through analyzing individual records only. An effective and comparatively
simple way of solving group anonymity problem is doubtlessly applying wavelet
transform. It's easy-to-implement, powerful enough, and might produce
acceptable results if used properly. In the paper, we present a novel method of
using wavelet transform for providing group anonymity; it is gained through
redistributing wavelet approximation values, along with simultaneous fixing
data mean value and leaving wavelet details unchanged (or proportionally
altering them). Moreover, we provide a comprehensive example to illustrate the
method.
