Context enriched data for machine learning model

A device, the device for data sensitivity classification, comprising:

• a training set, the training set of data items and known attributes and features;
• an interface for receiving known attributes for the features of each data item based on gathered contextual information;
• a processor for building a learning model based on the received known attributes and corresponding data items;
• and
• the processor configured to employ the learning model as an initial rendering of a model, the model of the features and attributes;
• a data structure and processor responsive to the model, and an interface to a server farm for training and classifying data items according the model;
• an interface to a repository of the data items, each of the data items having at least one feature indicative of confidential, secret, or proprietary information in each of the data items;
• an interface to a plurality of sources, the interface configured to receive, from each of the plurality of sources, an attribute indicative of an inclusion of sensitive data in each of the data items;
• the model based on a plurality of the features denoting which attributes of the at least one features are an indication that each of the data items is likely to contain sensitive information, the attributes external to the training set and indicative of a greater or lesser likelihood that a data item contains sensitive or private data;
• and
• a server configured for invoking a model of the at least one features and attributes for computing whether each of the data items is a sensitive data item, based on the model defining metadata indicating a form and use of the plurality of data items.

A computer program embodying program code on a non-transitory medium that, when executed by a processor, performs steps for implementing a method of classifying data sensitivity in a data set, the method comprising:

• gathering a training set, the training set of data items and known attributes and features;
• receiving known attributes for the features of each data item based on gathered contextual information;
• building a learning model based on the received known attributes and corresponding data items;
• and
• employing the learning model as an initial rendering of a model, the model of the features and attributes;
• identifying a set of features that define a context for a plurality of data items in the data set, each feature of the set of features defining metadata about a form and use of the data items;
• determining, for each feature, a source for identifying an attribute for said each feature;
• computing, for each feature, an attribute indicative of a likelihood that each of the plurality of data items contains sensitive data based on referencing the source;
• associating a respective attribute of the computed attributes with each data item in the data set to generate an enriched data set including the attributes for each data item in the plurality of data items, the attributes external to the data set and indicative of a greater or lesser likelihood that a data item contains sensitive or private data;
• and
• concluding, based on the model defining metadata indicating a form and use of the plurality of data items, whether each of the plurality of data items is a sensitive data item.