The objective of this report is to present two algorithms which transform data generated by a random number generator into data satisfying certain constraints on means and variance-covariance structure. One algorithm uses a linear transformation and translation to force data generated from a multivariate normal distribution to have a specific mean vector and variance-covariance matrix. The other algorithm uses a series of additions and subtractions to ensure that data generated from a uniform distribution has a certain mean and variance. Data sets such as these may be beneficial when used for introducing noise in order to mask microdata as a avoidance technique.