Using ‘Faked’ Data is Key to Allaying Big Data Privacy Concerns

MIT is out of the blocks first once again with a technological development designed to fix some of the privacy issues associated with big data.

In a world where data analytics and machine learning are at the forefront of technological advancement, big data is becoming a necessary lynchpin of that process. However, most organisations do not have the internal expertise to deal with algorithm development and thus have to outsource their data analytics. This raises many concerns regarding the dissemination of sensitive information to outsiders

The researchers at MIT have come up with a novel solution to these privacy issues. Their machine learning system can create “synthetic data” modelled on the data set which contains no real data and can be distributed safely to outsiders for development and education purposes.

The synthetic data is a structural and statistical analogue of the original data set but does not contain any real information regarding the organisation. However, it performs similarly in data analytical and stress testing and thus renders it the ideal substrate for developing algorithms and design testing in the data science milieu.

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