Abstract: Modeling techniques to classify data sets containing personal identifiable information (PII) comprising identifiers are provided. In one technique, multiple data sets are identified, each data set containing identifiers that were generated by a computer system and that qualify as PII of a known identifier (ID) type. For each of the multiple data sets, a model is generated based on that data set and added to a set of models. A target data set that contains identifiers that were generated by the computer system and that qualify as PII of an unknown ID type is identified. A target model is generated based on the target data set. For at least one model in the set of models, a similarity operation of that model and the target model is performed. Based on the similarity operation, it is determined whether to associate the ID type of that model with the target data set.

Abstract: Modeling techniques to classify data sets containing personal identifiable information (PII) comprising identifiers are provided. In one technique, multiple data sets are identified, each data set containing identifiers that were generated by a computer system and that qualify as PII of a known identifier (ID) type. For each of the multiple data sets, a model is generated based on that data set and added to a set of models. A target data set that contains identifiers that were generated by the computer system and that qualify as PII of an unknown ID type is identified. A target model is generated based on the target data set. For at least one model in the set of models, a similarity operation of that model and the target model is performed. Based on the similarity operation, it is determined whether to associate the ID type of that model with the target data set.