If both distributions overlap perfectly this metric is 1, and it’s 0 if no overlap is found. Hazy is an AI based fintech company that generates smart synthetic data that’s safe to use, and works as a drop in replacement for real data science and analytics workloads. 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. Hazy has pioneered the use of synthetic data to solve this problem by providing a fully synthetic data twin that retains almost all of the value of the original data but removes all the personally identifiable information. Any model should be able to generate synthetic data with a Histogram Similarity score above 0.80, with an 80 percent histogram overlap. Because synthetic data is a relatively new field, many concerns are raised by stakeholders when dealing with it — mainly on quality and safety. 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. Run analytics workloads in the cloud without exposing your data. Information can be counterintuitive. Today we will explain those metrics that will bring rigour to the discussion on the quality of our synthetic data. Our synthetic data use cases include: cloud analytics, external analytics, data innovation, data monetisation, and data sourcing. Hazy generates statistically controlled synthetic data that can fix class imbalance, unlock data innovation and help you predict the future. We work with financial enterprises on reducing the number of false positives in their fraud detection workflow whilst catching the same amount of fraud. Hazy has 26 repositories available. We use advanced AI/ML techniques to generate a new type of smart synthetic data that's both private and safe to work with and good enough to use as a drop in replacement for real world data science workloads. We generate synthetic data for training fraud detection and financial risk models. The few datasets that are currently considered, both for assessment and training of learning-based dehazing techniques, exclusively rely on synthetic hazy images. Our synthetic data use cases include: cloud analytics, external analytics, data innovation, data monetisation, and data sourcing. The same for Y = 2 bits, so Y (blood pressure) is more informative about skin cancer than X (blood type). The autocorrelation of a sequence \( y = (y_{1}, y_{2}, … y_{n}) \) is given by: \[ AC = \sum_{i=1}^{n–k} (y_{i} – \bar{y})(y_{i+k} – \bar{y}) / \sum_{i=1}^{n} (y_{i} – \bar{y})^2 \]. Generating Synthetic Sequential Data Using GANs August 4, 2020 by Armando Vieira Sequential data — data that has time dependency — is very common in business, ranging from credit card transactions to medical healthcare records to stock market prices. Typically Hazy models can generate synthetic data with scores higher than 0.9, with 1 being a perfect score. Formal differential privacy guarantees that ensure individual-level privacy and can be configured to optimise fundamental privacy vs utility trade-offs. “Hazy has the potential to transform the way everyone interacts with Microsoft’s cloud technology and unlock huge value for our customers.”, “By 2022, 40% of data used to train AI models will be synthetically generated.”, “At Nationwide, we’re using Hazy to unlock our data for testing and data science in a way that signicantly reduces data leakage risk.”. 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. For instance, if we query the data for users above 50 years old and an annual income below £50,000, the same number of rows should be retrieved as in the original data. Hazy is a UCL AI spin out backed by Microsoft and Nationwide. Let’s explore the following example to help explain its meaning. Hazy synthetic data generation lets you create business insights across company, legal and compliance boundaries – without moving or exposing your data. Autocorrelation basically measures how events at time \( X(t) \) are related to events at time \( X(t - \delta) \) where \( \delta \) is a lag parameter. We specialise in the financial services data domain. Sign up for our sporadic newsletter to keep up to date on synthetic data, privacy matters and machine learning. An enterprise class software platform with a track record of successfully enabling real world enterprise data analytics in production. Hazy has 26 repositories available. Normally this involves splitting the data into a Training Set to train the model and a Test Set to validate the model, in order to avoid overfitting. Contribute to hazy/synthpop development by creating an account on GitHub. This metric compares the order of feature importance of variables in the same model as trained on the original data and on trained synthetic data. Hazy for Cross-Silo Analyse data across silos Problem data stuck in different silos (legal, geography, department, data centre, database system) can’t merge and analyse to get cross-silo insight Solution train synthetic data generators at the edge, in each silo sync generators and aggregate synthetic data, with These models can then be moved safely across company, legal and compliance boundaries. To evaluate these quantities we simply compute the marginals of X and Y (sums over rows and columns): And then the information H for variable X is obtained by summing over the marginals of X, \[- \sum_{i=1, 4} pi.log_{2} (pi) = 7/4 bits. Synthetic data sometimes works hand-in-hand with differential privacy, which essentially describes Hazy’s approach. Synthetic data innovation. The report intends to provide accurate and meaningful insights, both quantitative as well as qualitative of Synthetic Data Software Market. Once you onboard us, you can then spin up as many synthetic data sets as you want which you can then release to your prospects. Hazy – Fraud Detection. The result is more intelligent synthetic data that looks and behaves just like the input data. Hazy | 1 429 abonnés sur LinkedIn. The Mutual Information score is calculated for all possible pairs of variables in the data as the relative change in Mutual Information between the original to the synthetic data: \[ MI_{score} = \sum_{i=1}^{N} \sum_{j=1}^{N} \left[ \frac{ MI(x_{i},x_{j}) } { MI(\hat{x_{i}},\hat{x_{j}}) } \right] How do you know that the synthetic data preserves the same richness, correlations and properties of the original data? Where \( \bar{y} \) is the mean of \( y \). The synthetic data should preserve this temporal pattern as well as replicate the frequency of events, costs, and outcomes. Evaluate algorithms, projects and vendors without data governance headaches. This can carry over to machine learning engineers who can better model for this sort of future-demand scenarios. Armando Vieira is a PhD has a Physics and is being doing Data Science for the last 20 years. 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. Synthetic data comes with proven data compliance and risk mitigation. Synthetic data of good quality should be able to preserve the same order of importance of variables. And synthetic data allows orgs to increase speed to decision making, without risking or getting blocked on real data. Before then being used to generate statistically equivalent synthetic data. Learn more about Hazy synthetic data generation and request a demo at Hazy.com. Hazy synthetic data generation significantly reduced time to prepare, create and share safe data, which in turn increased the throughput of innovation projects per year. Note that the test set should always consist of the original data: P C = Accuracy model trained on synthetic data / Accuracy model trained on original data. Access, aggregate and integrate synthetic data from internal and external sources. where \(x\) is the original data and \(\hat{x}\) is the synthetic data. Most machine learning algorithms are able to rank the variables in that data that are more informative for a specific task. Good synthetic data should have a Mutual Information score of no less than 0.5. Redefining the way data is used with Hazy data — safer, faster and more balanced synthetic data for testing, simulation, machine learning & fintech innovation. Suppose we want to evaluate the Mutual Information between X (blood type) and Y (blood pressure) as a potential indicator for the likelihood of skin cancer. Hazy. Hazy synthetic data is already being used at major financial institutions for app developers to simulate realistic client behavior patterns before there are even users. Hazy uses advanced generative models to distill the signal in your data before condensing it back into safe synthetic data. As can be seen in Figure 4 the data has a complex temporal structure but with strong temporal and spatial correlations that have to be preserved in the synthetic version. Hazy for Cross-Silo Analyse data across silos Problem data stuck in different silos (legal, geography, department, data centre, database system) can’t merge and analyse to get cross-silo insight Solution train synthetic data generators at the edge, in each silo sync generators and aggregate synthetic data… 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. Follow their code on GitHub. Hazy synthetic data generation is built to enable enterprise analytics. \[ H(X) – H(X | Y) = 2 – 11/8 = 0.375bits \]. Through the testing presented above, we proved that GANs present as an effective way to address this problem. How can we be sure the synthetic data is really safe and can’t be reverse engineered to disclose private information. Since 2017, Harry and his team have been through several Capital Enterprise programmes, including ‘Green Light’, a programme run by CE and funded by CASTS. Access specialist external data analysts and externally hosted tools and services. Advanced generative models that can preserve the relationships in transactional time-series data and real-world customer CIS models. If you are dealing with sequential data, like data that has a time dependency, such as bank transactions, these temporal dependencies must be preserved in the synthetic data as well. 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. To capture these short and long-range correlations the metric of choice is Autocorrelation with a variable lag parameter. Author of the book "Business Applications of Deep Learning". That's drop-in compatible with your existing analytics code and workflows. 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. 2 talking about this. This dataset contains records of EEG signals from 120 patients over a series of trials. Using synthetic data, financial firms can increase the speed of innovation while maintaining control of information and avoiding the risk of a data security breach. \]. Synthetic data use cases. Hazy helped the Accenture Dock team deliver a major data analytics project for a large financial services customer. With this in mind, Hazy has five major metrics to assess the quality of our synthetic data generation. In 2018, Hazy won the $1 million Microsoft Innovate.AI prize for the best AI startup in Europe. Synthetic data use cases. Armando Vieira Data Scientist, Hazy. Zero risk, sample based synthetic data generation to safely share your data. Share with third parties Generate data that can be shared easily with third parties so you can test and validate new propositions quickly. For example, the fintech industry prevents the collection of real user data, as it poses a high risk of fraudulence. Synthetic data enables fast innovation by providing a safe way to share very sensitive data, like banking transactions, without compromising privacy. http://hazy.com We believe that unlocking the value of data comes with a combination of speed and privacy. 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. Mutual Information is not an easy concept to grasp. 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. However, some caution is necessary as, in some cases, a few extreme cases may be overwhelmingly important and, if not captured by the generator, could render the synthetic data useless — like rare events for fraud detection or money laundering. It originally span out of UCL just two years ago, but has come a long way since then. Advanced GAN technology Hazy Generate incorporates advanced deep learning technology to generate highly accurate safe data. Accenture were aiming to provide an advanced analytics capability. To illustrate Autocorrelation, we consider the following EEG dataset because brainwaves are entirely unique identifiers and thus exceptionally sensitive information. Hazy generates smart synthetic data that helps financial service companies innovate faster. Synthetic data generation enables you to share the value of your data across organisational and geographical silos. 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. Synthetic sequential data generation is a challenging problem that has not yet been fully solved. After removing personal identifiers, like IDs, names and addresses, Hazy machine learning algorithms generate a synthetic version of real data that retains almost the same statistical aspects of the original data but that will not match any real record. For instance, we may use the synthetic data to predict the likelihood of customer churn using, say, an XGBoost algorithm. In 2018, Hazy won the $1 million Microsoft Innovate.AI prize for the best AI startup in Europe. Iterate on ideas rapidly. Assuming data is tabular, this synthetic data metric quantifies the overlap of original versus synthetic data distributions corresponding to each column. However, their ability to do so was blocked by data access constraints. It’s important to our users that they are able to verify the quality of our synthetic data before they use it in production. Hazy generated a synthetic version of their customer’s data that preserved the core signal required for the analytics project. The metrics above give a good understanding of the quality of synthetic data. In the series of events (head, tails) of tossing a coin each realization has maximum information (entropy) — it means that observing any length of past events would not help us predict the very next event. Each sample contains measurements from 64 electrodes placed on the subjects’ scalps which were sampled at 256 Hz (3.9-msec epoch) for 1 second. For instance, in healthcare the order of exams and treatments must be preserved: chemotherapy treatments must follow x-rays, CT scans and other medical analysis in a specific order and timing. Read about how we reduced time, cost and risk for Nationwide Building Society by enabling them to generate highly representative synthetic data for transactions. I recently cohosted a webinar on Smart Synthetic Data with synthetic data generator Hazy’s Harry Keen and Microsoft’s Tom Davis, where we dove into the topic. Hazy – Fraud Detection. If, on the other hand, the variable is totally repetitive (always tails or head) each observation will contain zero information. This unblocked Accenture’s ability to analyse the data and deliver key business insight to their financial services customer. Using synthetic data, financial firms can increase the speed of innovation while maintaining control of information and avoiding the risk of a data security breach. \]. 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. is the entropy, or information, contained in each variable. This is a reimplementation in Python which allows synthetic data to be generated via the method .generate() after the algorithm had been fit to the original data via the method .fit(). 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. Hazy is a synthetic data company. 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. Hazy synthetic data quality metrics explained By Armando Vieira on 15 Jan 2021. We work with financial enterprises on reducing the number of false positives in their fraud detection workflow whilst catching the same amount of fraud. 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? Sign up for our sporadic newsletter to keep up to date on synthetic data, privacy matters and machine learning. “Synthetic Data Software Industry Report″ is a direct appreciation by The Insight Partners of the market potential. A further validation of the quality of synthetic data can be obtained by training a specific machine learning model on the synthetic data and test its performance on the original data. 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