A neural network approach for data masking

In this letter we present a neural network based data masking solution, in which the database information remains internally consistent yet is not inadvertently exposed in an interpretable state. The system differs from the classic data masking in the sense that it can understand the semantics of the original data and mask it using a neural network which is a priori trained by some rules. Our adaptive data masking (ADM) concentrates on data masking techniques such as shuffling, substitution, masking and number variance in an intelligent fashion with the help of adaptive neural network. The very nature of being adaptive makes data masking easier and content agnostic, and thus finds place in various vertical domains and systems.

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