\]. http://hazy.com We believe that unlocking the value of data comes with a combination of speed and privacy. For that purpose we use the concept of Mutual Information that measures the co-dependencies — or correlations if data is numeric — between all pairs of variables. Histogram Similarity is the easiest metric to understand and visualise. Histogram Similarity is important but it fails to capture the dependencies between different columns in the data. Mutual Information is not an easy concept to grasp. The report intends to provide accurate and meaningful insights, both quantitative as well as qualitative of Synthetic Data Software Market. These models can then be moved safely across company, legal and compliance boundaries. Another blogpost will tackle the essential privacy and security questions. The autocorrelation of a sequence \( y = (y_{1}, y_{2}, … y_{n}) \) is given by: \[ AC = \sum_{i=1}^{n–k} (y_{i} – \bar{y})(y_{i+k} – \bar{y}) / \sum_{i=1}^{n} (y_{i} – \bar{y})^2 \]. Formal differential privacy guarantees that ensure individual-level privacy and can be configured to optimise fundamental privacy vs utility trade-offs. Even more challenging is the replication of seemingly unique events, like the Covid-19 pandemic, which proves itself a formidable challenge for any generative model. In 2018, Hazy won the $1 million Microsoft Innovate.AI prize for the best AI startup in Europe. If the synthetic data is of good quality, the performance of the model yp measured by accuracy or AUC, trained on synthetic data versus the one trained on original data, should be very similar. Hazy synthetic data can be used for zero risk advanced machine learning and data reporting / analytics. Patrick saw the potential for Hazy to help solve this challenge with synthetic data, reducing the risk of using sensitive customer data and reducing the time it takes for a customer to provision safe data for them to work on. where \(x\) is the original data and \(\hat{x}\) is the synthetic data. 2 talking about this. The following table contains hypothetical probabilities of skin cancer for all combinations of X and Y: The question is: how much information does each variable contain and how much information can we get from X, given Y? The Hazy team has built a sophisticated synthetic data generator and enterprise platform that helps customers unlock their data’s full potential, increasing the speed at which they are able to innovate, while minimising risk exposure. We generate synthetic data for training fraud detection and financial risk models. The next figure shows an example of mutual information (symmetric) matrix: When we developed this MI score alongside Nationwide Building Society, we were building on the work of Carnegie Mellon University’s DoppelGANger generator, which looks to make differentially private sequential synthetic data. If, on the other hand, the variable is totally repetitive (always tails or head) each observation will contain zero information. To address this limitation, we introduce the first outdoor scenes database (named O-HAZE) composed of pairs of real hazy and corresponding haze-free images. Today we will explain those metrics that will bring rigour to the discussion on the quality of our synthetic data. \]. We use advanced AI/ML techniques to generate a new type of smart synthetic data that’s safe to work with and good enough to use as a drop in replacement for real world data science workloads. The DoppelGANger generator had hit a 43 percent match, while the Hazy synthetic data generator has so far resulted in an 88 percent match for privacy epsilon of 1. To illustrate Autocorrelation, we consider the following EEG dataset because brainwaves are entirely unique identifiers and thus exceptionally sensitive information. Synthetic data enables data scientists and developers to train models for projects in areas where big data capability is not available or if it is difficult to access due to its sensitivity. Hazy generates smart synthetic data that's safe to use, allowing companies to innovate with data without using anything sensitive or real-life. Hazy. This can carry over to machine learning engineers who can better model for this sort of future-demand scenarios. Whatever the metric or metrics our customers choose, we are happy that they are able to check the quality of our synthetic data for themselves, building trust and confidence in Hazy’s world-class, enterprise-grade generators. Hazy is the most advanced and experienced synthetic data company in the world with teammates on three continents. Hazy is a synthetic data generation company. In other words, the synthetic data keeps all the data value while not compromising any of the privacy. Our core product is synthetic data - data generated artificially using machine learning techniques, that retains the statistical properties of the real data and can be safely used for analytics and innovation without compromising customers privacy and confidential information. 1, and outcomes each column those metrics that will bring rigour to the uncertainty or randomness of variable! 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