Providing privacy for natural language text data remains a largely open problem, despite its great practical importance. The current state of the art is manual redaction of sensitive words such as names, addresses etc. In this paper we propose viewing a corpus of text as a probability distribution over sequences of words. A sentence is then one realization from this distribution and redacting words changes the probability distribution. We use the Renyi-divergence divergence as a measure of the distance between two redacted datasets. We show that if enough words are redacted then sensitive redacted text can be made be statistically indistinguishable from non-sensitive redacted text. This can be used to develop efficient redaction strategies, that minimise the amount of redaction while meeting a privacy target.