Identification of pseudonymized data within data sources
A computer-implemented method, computer program product and system for identifying pseudonymized data within data sources. One or more data repositories within one or more of the data sources are selected. One or more privacy data models are provided, where each of the privacy data models includes pattern(s) and/or parameter(s). One or more of the one or more privacy data models are selected. Data identification information is generated, where the data identification information indicates a presence or absence of pseudonymized data and of non-pseudonymized data within the one or more of the data sources. The data identification information is generated utilizing the pattern(s) and/or the parameter(s) to determine pseudonymized data.
1. A computer-implemented method for identifying pseudonymized data within data sources, the method comprising:
selecting one or more data repositories within one or more of said data sources;
providing one or more privacy data models, each of said privacy data models comprising one or both of one or more patterns and one or more parameters;
selecting one or more of said one or more privacy data models;
analyzing data stored in said one or more selected data repositories using one or more parameters from said selected one or more privacy data models, wherein said analysis using said one or more parameters from said selected one or more privacy data models comprises identifying a meaning of each individual data value within said one or more selected data repositories, wherein said meaning of each individual data value within said one or more selected data repositories is determined by using cognitive semantics to identify an absolute frequency of specific data values within said one or more selected data repositories;
calculating a frequency distribution table containing frequency distribution values based on said identified absolute frequency of specific data values within said one or more selected data repositories;
comparing said frequency distribution values of said frequency distribution table to a predefined threshold value to determine a presence or absence of pseudonymized data; and
determining a data value within a data source to be pseudonymized in response to its associated frequency distribution value exceeding said predefined threshold value.
2. The computer-implemented method as recited claim 1 further comprising:
generating notifications for said pseudonymized data within said one or more of said data sources.
3. The computer-implemented method as recited in claim 1 , wherein said one or more patterns correspond to an encoded cryptographic digest.
4. The computer-implemented method as recited in claim 1 , wherein said one or more parameters correspond to absolute frequency information associated with specific data values.
5. The computer-implemented method as recited in claim 1 , wherein said one or more parameters comprise a user parameter.
6. The computer-implemented method as recited claim 1 further comprising:
analyzing data stored in said one or more selected data repositories using one or more patterns from said selected one or more privacy data models, wherein said analysis using said one or more patterns from said selected one or more privacy data models comprises using anonymization deconstruction techniques; and
identifying pseudonymized data by deconstructing cryptographic digests.