Transforming Customer Content Data to Anonymized System Metadata via k-Aggregation

An application service provider server, comprising:

• a processor;
• an enterprise application;
• a communication connection for connecting remote computing systems to the application service provider server via a network, wherein the remote computing systems are operated by users associated with multiple enterprises;
• and
• a computer-readable storage medium operatively coupled to the processor, the computer-readable storage medium comprising computer-executable instructions that, when executed by the processor, cause the processor to:
• cause execution of the enterprise application on the remote computing systems;
• log customer content data comprising data samples corresponding to each user's interactions with the enterprise application, wherein the data samples for each user are sorted by position;
• perform k-aggregation of the data samples by:
• randomly selecting an enterprise from the multiple enterprise;
• randomly selecting a user associated with the selected enterprise from the users of the remote computing systems;
• randomly selecting a data sample corresponding to the selected user;
• repeating the random selection of the enterprise, the random selection of the user, and the random selection of the data sample a first predetermined number (k) of times, wherein the repetition of the random selection of the enterprise and the random selection of the user is performed without replacement;
• and
• aggregating the randomly-requested data samples by position to generate an aggregated data sample;
• repeat the performance of the k-aggregation of the data samples a second predetermined number (N) of times with replacement to generate N aggregated data samples;
• concatenate the N aggregated data samples to generate an anonymized dataset that is classified as system metadata;
• train a machine learning model using the anonymized dataset;
• and
• deploy the trained machine learning model via the enterprise application.

A computer-readable storage medium comprising computer-executable instructions that, when executed by a processor, cause the processor to:

• cause execution of an enterprise application on remote computing systems operated by users associated with multiple enterprises;
• log customer content data comprising data samples corresponding to each user's interactions with the enterprise application, wherein the data samples for each user are sorted by position;
• perform k-aggregation of the data samples by:
• randomly selecting an enterprise from the multiple enterprise;
• randomly selecting a user associated with the selected enterprise from the users of the remote computing systems;
• randomly selecting a data sample corresponding to the selected user;
• repeating the random selection of the enterprise, the random selection of the user, and the random selection of the data sample a first predetermined number (k) of times, wherein the repetition of the random selection of the enterprise and the random selection of the user is performed without replacement;
• and
• aggregating the randomly-requested data samples by position to generate an aggregated data sample;
• repeat the performance of the k-aggregation of the data samples a second predetermined number (N) of times with replacement to generate N aggregated data samples;
• concatenate the N aggregated data samples to generate an anonymized dataset that is classified as system metadata;
• train a machine learning model using the anonymized dataset;
• and
• deploy the trained machine learning model via the enterprise application.