Personal data anonymization with model verification

A system comprising:

• a memory comprising instructions;
• and
• one or more computer processors, wherein the instructions, when executed by the one or more computer processors, cause the system to perform operations comprising:
• generating training data associated with electronic messages to be transmitted by a plurality of users, wherein the training data correlates anonymized communications data created by anonymizing the electronic messages to be transmitted by the plurality of users to personally identifiable information (PII) extracted from the electronic messages to be transmitted by the plurality of users, wherein the electronic messages comprise one or more of email messages or short message service (SMS) messages, and wherein anonymizing the electronic messages comprises redacting text of a communication to delete one or more PII values and embedding the redacted text into a vector;
• training, based on the training data associated with the electronic messages to be transmitted by the plurality of users, a machine-learning program to generate a PII detection model that extracts PII values from input electronic messages;
• extracting, by the PII detection model executing on the one or more computer processors, PII values from anonymized testing electronic messages;
• determining differences between the extracted PII values obtained from the PII detection model and randomly generated PII values obtained from a random value model that randomly generates PII values using a list of PII values found in the anonymized testing electronic messages based on frequencies of appearance;
• and
• responsive to the determined differences between the extracted PII values and the randomly generated PII values being below a predetermined confidence level, determining that anonymization of the electronic messages is valid based on the PII detection model;
• and
• causing spam detection to be performed using the electronic messages anonymized based on the PII detection model.

A non-transitory machine-readable storage medium including instructions that, when executed by one or more processors of a machine, cause the machine to perform operations comprising:

• generating training data associated with electronic messages to be transmitted by a plurality of users, wherein the training data correlates anonymized communications data created by anonymizing the electronic messages to be transmitted by the plurality of users to personally identifiable information (PII) extracted from the electronic messages to be transmitted by the plurality of users, wherein the electronic messages comprise one or more of email messages or short message service (SMS) messages, and wherein anonymizing the electronic messages comprises redacting text of a communication to delete one or more PII values and embedding the redacted text into a vector;
• training, based on the training data associated with the electronic messages to be transmitted by the plurality of users, a machine-learning program to generate a PII detection model that extracts PII values from input electronic messages;
• extracting, by the PII detection model executing on the one or more processors, PII values from anonymized testing electronic messages;
• determining differences between the extracted PII values obtained from the PII detection model and randomly generated PII values obtained from a random value model that randomly generates PII values using a list of PII values found in the anonymized testing electronic messages based on frequencies of appearance;
• and
• responsive to the determined differences between the extracted PII values and the randomly generated PII values being below a predetermined confidence level, determining that anonymization of the electronic messages is valid based on the PII detection model;
• and
• causing spam detection to be performed using the electronic messages anonymized based on the PII detection model.