Monitoring user status by comparing public and private activities

A computer-readable storage medium storing instructions that when executed by a computer cause the computer to perform a method for detecting anomalous correlations between public and private activities of a user, the method comprising:

• collecting public activity data and private activity data associated with the user;
• generating a series of feature pairs, each feature pair including a public feature vector generated from public activity data and a private feature vector generated from private activity data, wherein a respective feature pair corresponds to a respective point in time;
• generating a model, based on at least the series of feature pairs, to determine whether there is an anomaly in a correlation between the user's public activity data and the user's private activity data, wherein the model is associated with a normal correlation between the user's public activity data and the user's private activity data over a period of time associated with the series of feature pairs;
• collecting additional public activity data and additional private activity data associated with the user;
• applying the model to determine whether there is an anomaly in the correlation between the additional public activity data and the additional private activity data;
• and
• issuing an alert in response to detecting an anomaly in the correlation between the additional public activity data and the additional private activity data of the user.

A computing system for detecting anomalous correlations between public and private activities of a user, the system comprising:

• one or more processors,
• a computer-readable medium coupled to the one or more processors having instructions stored thereon that, when executed by the one or more processors, cause the one or more processors to perform operations comprising:
• collecting public activity data and private activity data associated with the user;
• generating a series of feature pairs, each feature pair including a public feature vector generated from public activity data and a private feature vector generated from private activity data, wherein a respective feature pair corresponds to a respective point in time;
• generating a model, based on at least the series of feature pairs, to determine whether there is an anomaly in a correlation between the user's public activity data and the user's private activity data, wherein the model is associated with a normal correlation between the user's public activity data and the user's private activity data over a period of time associated with the series of feature pairs;
• collecting additional public activity data and additional private activity data associated with the user;
• applying the model to determine whether there is an anomaly in the correlation between the additional public activity data and the additional private activity data;
• and
• issuing an alert in response to detecting an anomaly in the correlation between the additional public activity data and the additional private activity data of the user.