By Shivam Gupta, Cantata Health Solutions
Machine Learning (ML) systems have made major advancements in recent years and are constantly used in a wide range of applications like image processing, autonomous cars, speech and gesture recognition, credit card fraud detection, and smart healthcare, to name a few. There are hardly any areas of businesswhere ML has not been applied. Due to this range of applications and the accuracy of the ML systems,millions of dollars are being invested by private and government organizations across the globe [1]. The data collected by mobile devices and systems, universities, banks, corporate organizations, and even in our homes, which might be private or public is being used by these Machine Learning applications.
Sometimes private data needs to be stored in centralized locations in plain text for the algorithms to extract the feature or pattern and to build a model of that application using Machine Learning systems.
The associated threats are not only limited to the leakage of this private data to an insider of that organization or an outsider eavesdropping on the private data. In addition to this there is a possibility of extracting other confidential information about an individual or a whole company’s data even if the data is anonymized by methods like data masking, pseudonymization, or the dataset itself, and the model would not be accessible and result revealing the final results [1].