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This paper introduces data distortion by probability distribution, a probability distortion that involves three steps. The first step is to identify the underlying density function of the original series and to estimate the parameters of this density function. The second step is to generate a series of data from the estimated density function. And the final step is to map and replace the generated series for the original one. Because it is replaced by the distorted data set, probability distortion guards the privacy of an individual belonging to the original data set. At the same time, the probability distorted series provides asymptotically the same statistical properties as those of the original series, since both are under the same distribution. Unlike conventional point distortion, probability distortion is difficult to compromise by repeated queries, and provides a maximum exposure for statistical analysis.