Runtime control of automation accuracy using adjustable thresholds
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 program product for identifying pseudonymized data within data sources, the computer program product comprising one or more computer readable storage mediums having program code embodied therewith, the program code comprising the programming instructions for:
selecting one or more data repositories within one or more of said data sources;
selecting one or more of one or more privacy data models, wherein the selected one or more of the one or more privacy data models comprises one or more patterns and one or more predefined frequency distribution threshold values used to determine whether data is classified as pseudonymized data;
analyzing data stored in said one or more selected data repositories using a pattern of the one or more patterns and a predefined frequency distribution threshold value of the one or more predefined frequency distribution threshold values;
determining whether said analyzed data satisfies privacy data model specifications based at least in part on:
whether a text string in the analyzed data has a fixed character length and one or more ending characters that match the pattern, wherein the pattern is an encoded cryptographic digest; and
a comparison of a frequency distribution of specific data values in the analyzed data with the predefined frequency distribution threshold value; and
identifying said analyzed data as being pseudonymized in response to said analyzed data satisfying said privacy data model specifications.
2. The computer program product as recited in claim 1 , wherein the program code further comprises the programming instructions for:
generating notification regarding said analyzed data identified as being pseudonymized.
3. The computer program product as recited in claim 1 , wherein the program code further comprises the programming instructions for:
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.
4. The computer program product as recited in claim 3 , wherein the program code further comprises the programming instructions for:
calculating a frequency distribution table containing frequency distribution values based on identified absolute frequency of specific data values within said one or more selected data repositories.
5. The computer program product as recited in claim 4 , wherein the program code further comprises the programming instructions for:
comparing said frequency distribution values of said frequency distribution table to a the predefined frequency distribution threshold value to determine a presence or absence of pseudonymized data.
6. The computer program product as recited in claim 5 , wherein the program code further comprises the programming instructions for:
determining a data value within a data source to be pseudonymized in response to its associated frequency distribution value exceeding the predefined frequency distribution threshold value.
7. The computer program product as recited in claim 1 , wherein said analysis using said pattern of the one or more patterns comprises:
using anonymization deconstruction techniques; and
identifying pseudonymized data by deconstructing cryptographic digests.
8. A system, comprising:
a memory for storing a computer program for identifying pseudonymized data within data sources; and
a processor connected to said memory, wherein said processor is configured to execute the program instructions of the computer program comprising:
selecting one or more data repositories within one or more of said data sources;
selecting one or more of one or more privacy data models, wherein the selected one or more of the one or more privacy data models comprises one or more patterns and one or more predefined frequency distribution threshold values used to determine whether data is classified as pseudonymized data;
analyzing data stored in said one or more selected data repositories using a pattern of the one or more patterns and a predefined frequency distribution threshold value of the one or more predefined frequency distribution threshold values;
determining whether said analyzed data satisfies privacy data model specifications based at least in part on:
whether a text string in the analyzed data has a fixed character length and one or more ending characters that match the pattern, wherein the pattern is an encoded cryptographic digest; and
a comparison of a frequency distribution of specific data values in the analyzed data with the predefined frequency distribution threshold value; and
identifying said analyzed data as being pseudonymized in response to said analyzed data satisfying said privacy data model specifications.
9. The system as recited in claim 8 , wherein the program instructions of the computer program further comprise:
generating notification regarding said analyzed data identified as being pseudonymized.
10. The system as recited in claim 8 , wherein the program instructions of the computer program further comprise:
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.
11. The system as recited in claim 10 , wherein the program instructions of the computer program further comprise:
calculating a frequency distribution table containing frequency distribution values based on identified absolute frequency of specific data values within said one or more selected data repositories.
12. The system as recited in claim 11 , wherein the program instructions of the computer program further comprise:
comparing said frequency distribution values of said frequency distribution table to predefined the frequency distribution threshold value to determine a presence or absence of pseudonymized data.
13. The system as recited in claim 12 , wherein the program instructions of the computer program further comprise:
determining a data value within a data source to be pseudonymized in response to its associated frequency distribution value exceeding the predefined frequency distribution threshold value.
14. The system as recited in claim 8 , wherein said analysis using said pattern of the one or more patterns comprises:
using anonymization deconstruction techniques; and
identifying pseudonymized data by deconstructing cryptographic digests.