  Protecting the privacy of data-sets has become hugely important these days.
Many real-life data-sets like income data, medical data need to be secured
before making it public. However, security comes at the cost of losing some
useful statistical information about the data-set. Data obfuscation deals with
this problem of masking a data-set in such a way that the utility of the data
is maximized while minimizing the risk of the disclosure of sensitive
information. Two popular approaches to data obfuscation for numerical data
involves (i) data swapping and (ii) adding noise to data. While the former
masks well sacrificing the whole of correlation information, the latter gives
estimates for most of the popular statistics like mean, variance, quantiles,
correlation but fails to give an unbiased estimate of the distribution curve of
the original data. In this paper, we propose a mixed method of obfuscation
combining the above two approaches and discuss how the proposed method succeeds
in giving an unbiased estimation of the distribution curve while giving
reliable estimates of the other well-known statistics like moments,
correlation.
