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.
The MIT researchers, led by Kalyan Veeramachaneni, proposed a concept they call the Synthetic Data Vault (SDV). This describes a machine learning system that creates artificial data from an original data set. The goal is to be able to use the data to test algorithms and analytical models without any association to the organisation involved. He succinctly states that, “In a way, we are using machine learning to enable machine learning,"
The SDV achieves this using a machine learning algorithm called “recursive conditional parameter aggregation” which exploits the hierarchical organisation of the data and captures the correlations between multiple fields to produce a multivariate model of the data. The system learns the model and subsequently produces an entire database of synthetic data.
