  Data obfuscation deals with the 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. To protect data we address some ways that
may as well retain its statistical uses to some extent. One such way is to mask
a data with additive noise and revert to certain desired parameters of the
original distribution from the knowledge of the noise distribution and masked
data. In this project, we discuss the estimation of any desired quantile and
range of a quantitative data set masked with additive noise.
