1. A computer implemented method of re-identification risk measurement of a dataset, the method comprising: retrieving, by a server from a database, the dataset comprising personally identifiable information for a plurality of individuals, each individual having cross-sectional (L 1 ) data defining identifiable information and a plurality of entries of longitudinal (L 2 ) data associated with the L 1 data; reducing, by the server for each individual, multiple occurrences of a same L 2 data to a single feature with an addition of a count; grouping, by the sever, the plurality of individuals into L 1 equivalence classes based on L 1 data quasi-identifiers; ordering, by the server for individuals within each L 1 equivalence class, features of the individuals from most to least identifying; subsampling, by the server, multiple features for each individual; determining, by the server, a similarity measure by counting the individuals in the L 1 equivalence class whose features comprise a superset of the subsampled features for each individual; combining, by the server, multiple similarity measures into a single measure per individual; and determining, by the server, an overall risk measurement from the combined similarity measures.