Probabilistic Text Change Detection Using an Immune Model

We present a probabilistic approach for detecting and analyzing changes in natural language motivated by biological immune systems. Contrary to traditional methods based on message-digest algorithms and line-by-line comparisons of two files, the proposed algorithm employs an implicit negative representation of text segments in the form of detector strings. A characteristic property of the presented change detection method is that it allows the analysis to be done without revealing the full contents of the original data to the authenticator. Implications of this property to security applications are outlined and an experiment is conducted to show how several incremental changes to a collaboratively maintained document can be analyzed.