Mixed Data Fingerprinting with Principal Components Analysis

A method for fingerprinting a data owner dataset, wherein the dataset comprises a plurality of records each comprising a plurality of fields, the method comprising the steps of:

• a. selecting a subset of the records from the dataset to produce a data owner subset;
• b. applying principal components analysis to the data owner subset to produce a matrix of data owner subset eigenvalues and a matrix of data owner subset eigenvectors;
• and
• c. analyzing the eigenvectors to produce a set of scores that define observational values on the principal components of the data owner subset.

A method for determining if a wild file is derived from a data owner dataset, wherein each of the data owner dataset and the wild file comprise a plurality of records each comprising a plurality of fields, the method comprising the steps of:

• a. extracting a subset of the records from the data owner dataset to produce a data owner subset;
• b. extracting a subset of the records from the wild file corresponding to the records in the data owner subset to produce a wild file subset;
• c. applying principal components analysis to the data owner subset to produce a set of data owner subset eigenvalues and a matrix of data owner subset eigenvectors;
• d. applying principal components analysis to the wild file subset to produce a set of wild file subset eigenvalues and a matrix of wild file subset eigenvectors;
• e. analyzing each data owner subset eigenvector relative to a corresponding wild file subset eigenvector to produce a similarity score.